N50 statistic defines assembly quality in terms of  contiguity . Given a set of contigs, the  N50  is defined as the sequence length of the shortest contig at 50% of the total assembly length. It can be thought of as the point of half of the mass of the distribution; the number of  bases  from all contigs longer than the  N50  will be close to the number of bases from all contigs shorter than the  N50 . For example, consider 9 contigs with the lengths 2,3,4,5,6,7,8,9, and 10; their sum is 54, half of the sum is 27, and the size of the genome also happens to be 54. Then, 50% of this assembly would be 10 + 9 + 8 = 27 (half the length of the sequence). Thus the N50=8, which is the size of the contig which, along with the larger contigs, contain half of sequence of a particular genome. Note: When comparing N50 values from different assemblies, the assembly sizes must be the same size in order for N50 to be meaningful.


N50 can be described as a weighted median statistic such that 50% of the entire assembly is contained in contigs or scaffolds equal to or larger than this value.


Given a set of contigs, each with its own length, the  L50  is defined as count of smallest number of contigs whose length sum makes up half of genome size. From the example above the L50=3.


The  N90 statistic  is less than or equal to the  N50  statistic; it is the length for which the collection of all contigs of that length or longer contains at least 90% of the sum of the lengths of all contigs.


Note that  N50  is calculated in the context of the assembly size rather than the genome size. Therefore, comparisons of N50 values derived from assemblies of significantly different lengths are usually not informative, even if for the same genome.  To address this, the authors of the  Assemblathon  competition came up with a new measure called  NG50 .  The  NG50 statistic  is the same as  N50  except that it is 50% of the known or estimated genome size that must be of the NG50 length or longer. This allows for meaningful comparisons between different assemblies.  In the typical case that the assembly size is not more than the genome size, the NG50 statistic will not be more than the N50 statistic.


The  D50 statistic  (also termed  D50 test ) is similar to the  N50  statistic in definition though it is generally not used to describe genome assemblies.  The  D50  statistic is the lowest value  d  for which the sum of the lengths of the largest  d  lengths is at least 50% of the sum of all of the lengths. [ 2 ]


U50  is the length of the smallest contig such that 50% of the sum of all unique, target-specific contigs is contained in contigs of size U50 or larger. [ 1 ]


UL50  is the number of contigs whose length sum produces U50.


UG50  is the length of the smallest contig such that 50% of the reference genome is contained in unique, target-specific contigs of size UG50 or larger.


UG50%  is the estimated percent coverage length of the UG50 in direct relation to the length of the reference genome. The calculation is (100 × (UG50/Length of reference genome). The  UG50% , as a percentage-based metric, can be used to compare assembly results from different samples or studies.


Consider two fictional, highly simplified genome assemblies, A and B, that are derived from two different species. Assembly A contains six contigs of lengths 80  kbp , 70 kbp, 50 kbp, 40 kbp, 30 kbp, and 20 kbp. The sum size of assembly A is 290 kbp, the N50 contig length is 70 kbp because 80 + 70 is greater than 50% of 290, and the L50 contig count is 2 contigs. The contig lengths of assembly B are the same as those of assembly A, except for the presence of two additional contigs with lengths of 10 kbp and 5 kbp. The size of assembly B is 305 kbp, the N50 contig length drops to 50 kbp because 80 + 70 + 50 is greater than 50% of 305, and the L50 contig count is 3 contigs. This example illustrates that one can sometimes increase the N50 length simply by removing some of the shortest contigs or scaffolds from an assembly.


If the estimated or known size of the genome from the fictional species A is 500 kbp then the  NG50  contig length is 30 kbp because 80 + 70 + 50 + 40 + 30 is greater than 50% of 500. In contrast, if the estimated or known size of the genome from species B is 350 kbp then it has an NG50 contig length of 50 kbp because 80 + 70 + 50 is greater than 50% of 350.


N50  can be found mathematically for a list  L  of positive integers as follows:


For example: If  L  = (2, 2, 2, 3, 3, 4, 8, 8), then  L'   consists of six 2's, six 3's, four 4's, and sixteen 8's.  That is,  L'   has twice as many 2s as  L ; it has three times as many 3s as  L ; it has four times as many 4s; etc. The median of the 32-element set  L'   is the average of the 16th smallest element, 4, and 17th smallest element, 8, so the  N50  is 6.  We can see that the sum of all values in the list  L  that are smaller than or equal to the  N50  of 6 is 16 = 2+2+2+3+3+4 and the sum of all values in the list  L  that are larger than or equal to 6 is also 16 = 8+8.  For comparison with the  N50  of 6, note that the mean of the list  L  is 4 while the median is 3.
To recapitulate in a more visual way, we have:


Values of the list        L  =  (2,    2,    2,    3,       3,       4,          8,                      8)


Values of the new list    L'   = (2  2  2  2  2  2  3  3  3  3  3  3  4  4  4   4    8   8  8  8  8  8  8  8  8  8  8  8  8  8  8  8)


Ranks of  L'   values =           1  2  3  4  5  6  7  8  9  10 11 12 13 14 15  16   17  18 19 20 21 22 23 24 25 26 27 28 29 30 31 32


The  National Center for Integrative Biomedical Informatics  (NCIBI) is one of seven  National Centers for Biomedical Computing  funded by the  National Institutes of Health 's (NIH) Roadmap for Medical Research. [ 1 ] [ 2 ]  The center is based at the  University of Michigan  and is part of the  Center for Computational Medicine and Bioinformatics .  NCIBI's mission is to create targeted knowledge environments for molecular biomedical research to help guide experiments and enable new insights from the analysis of complex diseases. It was established in October 2005.


The Center develops computational methods to effectively access and integrate biological data. Driving Biological Projects (DBPs) provide a starting point from which tool development is informed, launched, and tested. Current DBPs include  gene fusion  in cancers, major organ-specific complications of  diabetes , nutrition and  obesity , and co-morbid disease associations of  bipolar disorder .  In addition to testing tools for function, a separate team is dedicated to testing  usability  and user interaction.


Once tools are developed and validated, the Center disseminates data and software throughout the University and the broader biomedical research community. Various mechanisms such as training videos, [ 3 ]  tutorials, [ 4 ]  and demonstrations and presentations at prominent scientific conferences are used to share NCIBI data and software nationally and internationally. [ 5 ]


In addition to the tools listed below, the enriched data contained in many NCIBI databases is available from the Databases tab on the Try Our Tools page. [ 6 ]


The  National Centers for Biomedical Computing  ( NCBCs ) are part of the U.S.  National Institutes of Health  plan to develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. Their mission is to create innovative software programs and other tools that will enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease.


Recognizing the potential benefits to human health that can be realized from applying and advancing the field of biomedical computing, the Biomedical Information Science and Technology Initiative (BISTI) was launched at the  NIH  in April 2000. This initiative is aimed at making optimal use of computer science and technology to address problems in biology and medicine. The full text of the original BISTI Report is available. [ 1 ]


As of April 2016, the web site for the National Centers for Biomedical Computing (http://www.ncbcs.org) is no longer managed by that organization, though many of the centers still are supported. [ citation needed ]


The  National Institute of Genetics  ("Japanese Institute of Genetics") is a Japanese institution founded in 1949. [ 1 ]


It hosts the  DNA Data Bank of Japan .


The  Epigenomics database  at the  National Center for Biotechnology Information  was a database for whole-genome  epigenetics  data sets. [ 1 ]  It was retired on 1 June 2016.


The Epigenomics database of the  National Center for Biotechnology Information  (NCBI) at the  National Institutes of Health  (NIH) was launched in June 2010 as a means to collect maps of epigenetic modifications and their occurrence across the human genome. [ 2 ]  This database provides a publicly available resource for maps in  stem cells  and primary  ex vivo  tissues that detail genome-wide landscapes of epigenetic factors that occur in human development and disease. [ 1 ]


The primary resources for the content of the Epigenomics Database are derived from two archival databases at the NCBI: The  Gene Expression Omnibus  (GEO) and the  Sequence Read Archive  (SRA). [ 1 ]   The Gene Expression Omnibus is a data system for high-throughput genomic data that is generated from  microarray  and  next-generation sequencing  technologies. [ 3 ]   Data used in the Epigenomics database is a combination of GEO and SRA subsets that are specific to Epigenetic factors. This data is subjected to additional review and organized in a more easily attainable fashion before added to the Epigenomics database. [ 1 ]


All of the experiments and corresponding samples in the Epigenomics database are displayed in the default browser. As of October 2013, there are currently 4112 experiments and 1257 samples available in the database. [ 4 ]  Five studied species are represented in the database, and many data tracks are available including expression of  micro  and  small RNAs ,  histone modification  and histone modifying enzymes, chromatin accessibility and chromatin associated factors, and  transcription factors . [ 1 ]  One such example from the database is a study of certain epigenetic factors in  Drosophila melanogaster  at the 20- to 24-hour embryonic stage of development. [ 5 ]


The Epigenomics database browser contain two fundamental search records, "Experiments" and "Samples".


The Experiment search record refers to one or more experiments with a set of scientific aims. [ 1 ]  Here a user is able to retrieve full data source information.  This information includes the institution of the submitter, links to the original data submissions in GEO and SRA, links to literature citations in  PubMed  and/or full text articles in Pubmed. [ 1 ]  Experiment records contain a unique accession number that includes a prefix 'ESS'. [ 1 ]


The sample search record corresponds to the biological material examined in a given experiment in the database and provides details about source attributes with values from controlled vocabularies. [ 1 ]  There are over 20 biological attribute fields available, and among these fields include strain, cultivar, ecotype, individual, gender, age, developmental stage, cell line, cell type, tissue type, and health status. [ 1 ]


There are many available resources online for help in navigating and using the Epigenomics database. The "Epigenetics Help" section of the NCBI help manual contains information on the searchable database and provides a user with tools to use, manage, download and upload, and navigate the database. [ 6 ]
There are also guides to navigation of the database aimed at specific researchers and fields of study, such as stem cell research. [ 7 ]


In 2007 the  National Institutes of Health  (NIH) launched the  Roadmap Epigenomics Project . The aim of the project is a development of publicly available reference epigenome maps from a variety of cell types. [ 1 ]  These epigenetic maps are intended to provide resources for studies of epigenetic events that underline human development, diversity, and disease. [ 8 ]  For similar efforts see the  ENCODE  (ENCyclopedia Of DNA Elements) Project, whose initiatives are complementary to the Roadmap Epigenomics Project. [ 9 ]


The  epigenome  consists of a record of the chemical changes to the  DNA  and  Histone  proteins of an organism.  These chemical changes influence gene expression across many tissue types and developmental stages. [ 10 ]  These  epigenetic  changes involve methods of altering gene expression that do not involve changes in the underlying primary DNA sequence; these include  DNA methylation ,  Gene silencing , and  chromatin  structure, [ 11 ]  as well as involvement of  Non-coding RNA . [ 12 ]


Neoepitopes  are a class of  major histocompatibility complex  (MHC) bounded  peptides . [ 1 ]  They represent the  antigenic determinants  of  neoantigens . Neoepitopes are recognized by the  immune system  as targets for  T cells  and can elicit  immune response  to  cancer . [ 2 ] [ 3 ]


Epitopes , also referred to as antigenic determinants, are parts of an  antigen  that are recognized by the immune system. A neoepitope is an epitope the immune system has not encountered before. Therefore it is not subject to  tolerance mechanisms  of the immune system. [ 4 ]  As the mutant  gene  product is only expressed in tumors and is not found in non-cancerous cells, neoepitopes may evoke a vigorous T cell response. [ 5 ]   Tumor Mutational Burden (TMB, the number of mutations within a targeted genetic region in the cancerous cell's DNA) correlates with the number of neoepitopes, and have been suggested to correlate with patient survival post immunotherapy, although the findings about the neoantigen/immunogenicity association are disputed. [ 6 ] [ 7 ] [ 8 ] [ 9 ]


Neoepitopes arise from  post-translational modifications . The  mRNA  translates information from the  DNA  into  polypeptide  composed of 20 standard  amino acids  and then  proteins . Several of the standard amino acids can be posttranslationally modified by  enzymatic  processes, or can be altered through spontaneous (nonenzymatic)  biochemical  reactions. [ 10 ]


There is increasing evidence that immune recognition of neoepitopes produced by cancer-specific mutations is a key mechanism for the induction of immune-mediated tumor rejection. Opportunities for therapeutic targeting of cancer specific neoepitopes are under investigation. [ 11 ]


Cancer is a patient-specific disease, and no two tumors are alike. Thus, the  immunogenicity  of each tumor is unique. [ 12 ]  A novel strategy against cancer is epitope selection for  mutanome -directed  individualized cancer immunotherapy . [ 4 ]


Individualized cancer immunotherapy leverages the  adaptive immune system  by targeting T cells to tumor cells that have a tumor specific mutant antigen ( neoantigen ) with neoepitopes recognized by a receptor on T cells. [ 13 ]  One challenge is to identify the neoepitopes that trigger a suitable immune response, that is, to find out which neoepitopes in the individual tumor are highly immunogenic. [ 14 ]


Individualized cancer immunotherapy includes  vaccination  with tumor mutation-derived neoepitopes. The concept is based on a  mapping  of the tumor-specific individual mutanome with identification of a range of suitable neoepitopes for a patient-specific  vaccine . [ 15 ]  It is expected that the neoepitopes in the vaccine will trigger T cell responses to the specific cancer. For the concept of individualized cancer vaccination first data are available. [ 16 ] [ 17 ] [ 18 ] [ 19 ]


The  Netherlands Bioinformatics Centre  or  NBIC [ 1 ]  (2003–2013) was a Dutch, government-funded organization, that coordinated all academic work on  bioinformatics  in the  Netherlands . [ 2 ]  NBIC consisted of programs for research (BioRange), for education (BioWise), and for support (BioAssist). NBIC partners are all  Dutch universities  and university hospitals, and some commercial entities. The NBIC central office was hosted by the  Center for Molecular and Biomolecular Informatics  (CMBI) at the  Radboud University Nijmegen Medical Centre .


In 2003 NBIC was launched [ 3 ]  as a  not-for-profit  organization, to strengthen and organize the bioinformatics research in the  Netherlands . NBIC is one of the four enabling technology centres that were initiated by the  Netherlands Genomics Initiative  (NGI) [ 4 ]  to improve the  genomics -based research in the Netherlands. The other three technology centres are:
the  Netherlands Proteomics Centre  (NPC), [ 5 ]  the  Netherlands Metabolomics Centre  (NMC) [ 6 ]  and the  Netherlands Consortium for Systems Biology  (NCSB). [ 7 ]  Most services moved to the Dutch Techcentre for Life Sciences in 2015. [ 8 ]


The Netherlands Bioinformatics Centre was funded by the  Dutch government  through the  Netherlands Genomics Initiative  (NGI) [ 9 ]  and the BSIK [ 10 ] (knowledge infrastructures) programme. [ citation needed ]


The support department of NBIC, BioAssist, consists of task forces that bring together experts in the different fields of Bioinformatics. Together, these people identify common problems in their field that they set out to solve in a collaborative way. BioAssist also has a group, BioInformatics Research Support (BRS), [ 11 ] [ 12 ]  that helps biological and medical professionals with short bioinformatics projects.


The education part of NBIC, BioWise, organizes courses and trainings at different levels. They provide awareness training at schools, [ 13 ]  collaborate with the different bioinformatics education programs at the universities in the Netherlands, and organize trainings for  PhD  students. Also, subject-specific trainings and conferences are organized, either nationally or with other international partners like the  Swiss Institute of Bioinformatics .


NBIC partnered with Dutch universities of Applied Sciences (UAS) in the LOBIN network. in July 2011, four UASes had joined. [ 14 ]


A wide variety of  bioinformatics software  has been created by people involved in NBIC. [ 15 ]  NBIC also hosted a service to coordinate  software  development (Trac server with software releases and project documentation). [ 16 ]  Many tools are also made available through NBIC's own open  Galaxy  server. [ 17 ]


First steps have been made to include commercial entities, among others, via the Bioinformatics Industrial Users Platform (BIUP). The BIUP, though still in its start-up phase in mid-2011, has already attracted partners such as  DSM .


A large part of NBIC's efforts focus on developing new or strengthening existing bio-informatics communities. For example, NBIC hosted the 2011  Galaxy  Community Conference. [ 18 ]
The  Netherlands Bioinformatics for Proteomics Platform  (for short NBPP) is a joint initiative of the Netherlands Bioinformatics Centre (NBIC) and the  Netherlands Proteomics Centre  (NPC).


The inaugural meeting of the  Global Organisation for Bioinformatics Learning, Education and Training (GOBLET)  was held at NBIC in November 2012. [ 19 ]


The  Neuroimaging Tools and Resources Collaboratory  ( NITRC ) is a neuroimaging informatics knowledge environment for MR, PET/SPECT, CT, EEG/MEG, optical imaging, clinical neuroinformatics, imaging genomics, and computational neuroscience tools and resources.


Initiated in 2006 and currently funded by NIH Grant number:  1R24EB029173  , [ 1 ] [ 2 ]  NITRC's mission is to provide a user-friendly knowledge environment that enables the distribution, enhancement, and adoption of neuroimaging tools and resources and has expanded from MR to Imaging Genomics, EEG/MEG, PET/SPECT, CT, optical imaging, clinical neuroinformatics, and computational neuroscience. Supporting 143,000 page views per month, NITRC's 1,000+ tools and resources have been downloaded over 11.4 million times by 1.4 million users.


NITRC's goal is to support researchers dedicated to enhancing, adopting, distributing, and contributing to the evolution of previously funded neuroimaging analysis tools and resources for broader community use. Promoting software tools, workflows, resources, vocabularies, test data, and pre-processed, community-generated images through its Image Repository (NITRC-IR), NITRC gives researchers greater and more efficient access to the tools and resources they need; better categorizing and organizing existing tools and resources via a controlled vocabulary; facilitating interactions between researchers and developers through forums, direct email contact, ratings and reviews; and promoting better use through enhanced documentation.


To meet the disparate needs of neuroimaging informatics developers and researchers, NITRC offers collaborative functionality like that found in platforms such as  GitHub  and  SourceForge . To provide such functionality, we customized the open-source  GForge  project. Thus, within NITRC, each tool or resource has the option to offer descriptive content as well as use MediaWiki, CVS/SVN, bug tracking, news, and forums to distribute information and downloads. Housed on NITRC servers and linked out to existing Web sites, NITRC is the source for neuroimaging informatics tools and resources.


Launched in 2007 with an agile programming schedule, the NITRC team continues to prioritize and implement functional and design enhancements to make the Web site even more accessible. While the scientific scope expands so do NITRC's Enhanced Services, which include its Image Repository to support image data sharing and its Computational Environment service on  Amazon Marketplace  to support the execution of complex computational analysis of image data.


The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) suite of services include:


NITRC Resource Repository (NITRC-R)  is the “go to” collaboration environment that enables the worldwide distribution, enhancement, and adoption of neuroinformatics tools and resources. NITRC-R's scientific scope includes MR, PET/SPECT, CT, EEG/MEG, optical imaging, clinical neuroinformatics, imaging genomics, and most recently, computational neuroscience.


NITRC Image Repository (NITRC-IR) , built on XNAT, is a curated repository of DICOM and NIfTI scanned images searchable by metadata such as diagnosis, handedness, gender, or group. NITRC-IR datasets include:  Functional Connectomes, Autism Brain Imaging Data Exchange (ABIDE), Sample, ADHD-200, Beijing Eyes Open Eyes Shut.


NITRC Computational Environment (NITRC-CE)  is a virtual big data compute service pre-configured with popular neuroimaging software analysis tools allowing pay-as-you-go compute time. Using AWS EC2, and leveraging NeuroDebian, NITRC-CE [ 3 ]  and NITRC-CE for Cluster Compute Instances [ 4 ]  are available via the AWS Marketplace. A public Amazon Machine Instance (AMI) is also available.


NITRC's triad of services serve the global neuroinformatics research community with 64% of its users coming from the United States, China, Germany, United Kingdom, and Canada. With over 3.2 million page views and 747,000 visits by 335,200 unique visitors, NITRC-R facilitates access to an ever growing number of neuroinformatics tools and resources (630).
NITRC-IR offers 4,800 Subjects searchable across 9 projects to promote re-use and integration of these valuable shared data.
Averaging 20,900 visits and 76,200 pageviews per month, software and data from NITRC-R and NITRC-IR have been downloaded over 1.3 million times.
NITRC-CE provides simplified deployment of cloud-based computation that supports FreeSurfer, FSL, AFNI, and many other software resources. In real-world processing tests, a representative computation that would have taken 24 hours on a high-powered desktop took 25% of the time (8 hours) at a cost of only $4. The test was a FSL voxel-based morphometry (VBM) computation on 64 subjects from CANDIShare run on a 2.8 GHz Intel Xeon Mac desktop versus AWS Large instance (m1.large) using SGE parallelization over 4 cores.


NITRC is led by University of Massachusetts Medical School in Worcester, MA; and is built and operated in collaboration with TCG, Inc. of Washington, DC; Preuss Enterprises, Inc., FL; and The Paulson Venture, CA. NITRC-R and NITRC-IR are hosted by the Center for Research in Biological Systems at the University of California, San Diego.  Current team members include: David Kennedy and Christian Haselgrove, UMMS; Nina Preuss, PMP, Preuss Enterprises Inc.; Matthew Travers and Al Crowley, TCG, Inc.; and Abby Paulson, The Paulson Venture.


Nullomers  are short sequences of  DNA  that do not occur in the genome of a species (for example, humans), even though they are theoretically possible. [ 1 ] [ 2 ]  Nullomers must be under selective pressure - for example, they may be toxic to the cell. [ 2 ]  Some nullomers have been shown to be useful to treat  leukemia ,  breast , and  prostate cancer . They are not useful in healthy cells because normal cells adapt and become immune to them. [ 2 ]  Nullomers are also being developed for use as  DNA tags  to prevent  cross contamination  when analyzing  crime scene  material. [ 3 ]


Nullomers are naturally occurring but potentially unused sequences of  DNA . Determining these "forbidden" sequences can improve the understanding of the basic rules that govern  sequence evolution . [ 4 ]   Sequencing entire genomes  has shown that there is a high level of non-uniformity in genomic sequences. When a codon is artificially substituted with a synonymous codon, it often results in a lethal change and cell death. This is believed to be due to ribosomal stalling and early termination of protein synthesis. For example, both AGA and CGA code for arginine in bacteria; however, bacteria almost never use AGA, and when substituted it proves lethal. [ 5 ]  Such  codon biases  have been observed in all species, [ 6 ]  and are examples of  constraints on sequence evolution . Other sequences may have selective pressure; for example, GG-rich sequences are used as sacrificial sinks for  oxidative damage  because  oxidizing agents  are attracted to regions with GG-rich sequences and then induce  strand breakage . [ 7 ]  Moreover, it has been shown that statistically significant nullomers (i.e. absent short sequences which are highly expected to exist) in virus genomes are  restriction recognition sites  indicating that viruses have probably got rid of these motifs to facilitate invasion of bacterial hosts. [ 8 ]   Nullomers Database  provides a comprehensive collection of minimal absent sequences from hundreds of species and viruses as well as the human and mouse proteomes.


Nullomers have been used as an approach to  drug discovery  and development. Nullomer peptides were screened for  anti-cancer action . Absent sequences have short polyarginine tails added to increase solubility and uptake into the cell, producing peptides called PolyArgNulloPs. One successful sequence, RRRRRNWMWC, was demonstrated to have lethal effects in  breast  and  prostate cancer . It damaged  mitochondria  by increasing  ROS  production, which reduced  ATP  production, leading to  cell growth  inhibition and  cell death . Normal cells show a decreased sensitivity to PolyArgNulloPs over time. [ 2 ]


Accidental transfer of biological material containing DNA can produce misleading results. This is a particularly important consideration in forensic and crime labs, where mistakes can cause an innocent person to be convicted of a crime. There was no way to detect if a reference sample was mislabeled as evidence or if a forensic sample is contaminated, but a nullomer barcode can be added to reference samples to distinguish them from evidence on analysis. Tagging can be carried out during sample collection without affecting genotype or quantification results. Impregnated filter paper with various nullomers can be used to soak up and store DNA samples from a crime scene, making the technology simple and effective. [ 3 ]  Tagging with nullomers can be detected—even when diluted to a million-fold and spilled on evidence, these tags are still clearly detected. [ 3 ]  Tagging in this way supports  National Research Council's  recommendations on quality control to reduce fraud and mistakes. [ 3 ]


Nutritional epigenetics  is a science that studies the effects of nutrition on gene expression and  chromatin  accessibility. [ 1 ] [ 2 ]  It is a subcategory of  nutritional genomics  that focuses on the effects of  bioactive  food components on  epigenetic  events. [ 3 ]


Changes to children’s genetic profiles caused by fetal nutrition have been observed as early as the  Dutch famine of 1944-1945 . [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ]  Due to  malnutrition  in pregnant mothers, children born during this famine were more likely to exhibit health issues such as heart disease, obesity, schizophrenia, depression, and addiction. [ 4 ] [ 5 ] [ 6 ]


Biologists  Randy Jirtle  and Robert A. Waterland became early pioneers of nutritional epigenetics after publishing their research on the effects of a pregnant mother’s diet on her offspring’s gene functions in the research journal  Molecular and Cellular Biology  in 2003. [ 9 ] [ 10 ]


Researchers in nutritional epigenetics study the interaction between  molecules  in food and molecules that control  gene expression , which leads to areas of focus such as dietary methyl groups and  DNA methylation . [ 10 ] [ 11 ] [ 12 ]  Nutrients and  bioactive  food components affect epigenetics by inhibiting  enzymatic  activity related to DNA methylation and  histone  modifications. [ 13 ]  Because methyl groups are used for suppression of undesirable genes, a mother’s level of dietary methyl consumption can significantly alter her child’s gene expression, especially during early development. [ 14 ]  Furthermore, nutrition can affect methylation as the process continues throughout an individual’s adult life. Because of this, nutritional epigeneticists have studied food as a form of molecular exposure. [ 1 ]


DNA methylation is the addition of a methyl group on a cytosine ring of DNA. [ 15 ]  Without methylation, issues could arise regarding genomic imprinting, X-chromosome inactivation, and suppression of transcription and transposition. [ 15 ]  When methylation is not present to suppress transcription and transposition, the lack thereof can contribute to the development of  cancer .


Bioactive  food components that influence epigenetic processes range from vitamins such as  A ,  B6 , and  B12  to alcohol and elements such as  arsenic ,  cadmium , and  selenium . [ 3 ]  Dietary methyl supplements such as extra  folic acid  and  choline  can also have adverse effects on epigenetic gene regulation. [ 1 ] [ 10 ]


Folate  is an essential water soluble vitamin that is naturally occurring in some foods. Folate can be found naturally at high levels in dark green leafy vegetables such as spinach, brussels sprouts and asparagus, as well as in liver. [ 16 ]  Folic acid is a man made form used to supplement certain foods. Enriched breads, flours, pastas, rice, and breakfast cereals are commonly supplemented with folic acid. [ 17 ]  In DNA methylation, folate serves as a source of carbon/ methyl group. [ 18 ]


Choline is a semi-essential nutrient that can be oxidized to  betaine . The betaine then functions as a methyl group donor in the process of DNA methylation. [ 19 ]  Choline can be found in animal products such as meat, eggs, poultry, fish and dairy as well as potatoes and green leafy vegetables. [ 20 ]


Researchers have considered that high-fat and low-protein diets during pregnancy can increase the risk of obesity in infants. [ 21 ]  The consumption of  phytochemicals  can also positively affect epigenetic-based mechanisms that inhibit  cancer cells . [ 22 ]  Research has also suggested a link between nutritional epigenetics and the  pathophysiology  of major  depressive disorder . [ 23 ]


All life on Earth is influenced by the different flows of its environment, yet in humans, different environmental conditions such as  poverty ,  alcohol ,  stress , malnutrition, exposure to  pollutants , man-made chemicals, and  synthetic drugs  can lead to epigenetic-related illnesses/diseases with certain disease-specific genes typically being activated or deactivated. [ 12 ]   The  epigenome  of an organism can be triggered by just about any environmental signal, including  climate change , food/water supply, plant nutrient, temperature etc.


It has been estimated that more than 60% of deaths in humans are related to nutritional or dietary factors rather than environmental triggers. [ 12 ] [ 8 ]  Based on a couple of studies from the Dutch Famine of 1944-1945, it is stated that starvation during pregnancy and subsequent health can result in, but not limited to a some health risks including  type II diabetes mellitus ,  cardiovascular disease ,  metabolic disorders  and decreased cognitive functions later in life. [ 7 ]


The maternal lineage or mother's health and nutritional habits during pregnancy isn't the only influence on the offspring's overall health. Further transmission via the paternal line is highly likely to occur by epigenetic modulation of the  spermatozoa's  nucleus. [ 7 ]  An example of this is  transgenerational  transmission by the paternal lineage. There is evidence that both the paternal and maternal diets influence metabolic phenotypes in the offspring through epigenetic information transmission. [ 7 ] [ 12 ]


Evidence of the generational transmission of epigenetic mechanisms in humans was first discussed by Champagne in 2008 in the context of maternal stress with food insecurity being one type of  stressor  that can impact gene expression via changes in  DNA methylation  patterns. [ 24 ]  Another type of stressor is a poor prenatal diet that results in nutritional insufficiency and fetal epigenetic reprogramming that creates the blueprint for the development of diseases later in a child’s life. [ 25 ] [ 26 ]  Depending on geographical region, food quality issues may impact epigenetic inheritance via changes in methylation patterns associated with dietary heavy metal exposures, especially in the case of  autism  and  attention deficit hyperactivity disorders (ADHD) . [ 27 ]


Food insecurity refers to the inability to access enough food to meet basic needs and is associated with an increased risk of birth defects associated with DNA methylation patterns. [ 28 ] [ 8 ]  An  expectant mother  who is food insecure will likely be under financial stress and unable to secure enough food to meet her nutritional needs. Her geographical location may be in a food desert where she is unable to access enough safe and nutritious food. Food deserts are linked to food insecurity and defined as areas of high-density fast-food restaurants and corner stores offering only unhealthy highly processed foods at low prices. [ 29 ]


Poor  prenatal  diet or unhealthy diet has been shown to affect DNA methylation patterns and contribute to the development of type 2 diabetes, ADHD, and early onset conduct problems in children. [ 30 ] [ 31 ]   Characteristics of an unhealthy prenatal diet leading to changes in DNA methylation patterns include the increased intake of high fat/sugar  ultra-processed food  products along with the inadequate intake of nutrient rich whole foods (e.g. fruits and vegetables). High-fat and low-protein diets during pregnancy can also increase the risk of obesity in infants. [ 32 ]  Dietary methyl supplements such as extra folic acid and choline can also have adverse effects on epigenetic gene regulation. [ 1 ] [ 10 ]  The current global food system is plagued by issues that adversely affect human health through multiple pathways with contaminated, unsafe, and altered foods being one of the most common factors associated with unhealthy diet. [ 33 ]   Low iron diets, or women who suffer from an  iron deficiency , have been shown to increase the likelihood of a  premature birth ,  low birth weight , and the increased possibility of  postpartum depression . [ 34 ]


Food quality issues vary from one geographic region to the next depending on country, food safety practices, and manufacturing and agricultural regulations regarding  heavy metal ,  pesticide residues , and other  hazardous exposures of concern . [ 35 ]  To reduce exposures to chemical hazards such as pesticide and heavy metal residues, the  World Trade Organization  (WTO) sponsored agreements between countries to establish codes of best practices, issued by the  Codex Alimentarius Commission , that attempt to guarantee the trade of safe food. [ 35 ]   Despite the best practices in use, heavy metal and pesticide residues are still found in the food supply. [ 36 ] [ 37 ]   Pre-natal and post-natal dietary exposures to inorganic mercury and lead residues resulting from unhealthy diets have been shown to consistently impact important gene behaviors in children with autism and ADHD. [ 38 ]  Prenatal organophosphate pesticide exposure has been shown to impact  DNA methylation  in genes associated with the development of cardio-metabolic diseases. [ 39 ]  Infection from food is a serious factor during pregnancy. Not in particular of what the mother eats, but that is just as important, but the way the food is prepared. A mother should cook all of the food thoroughly, especially meats. All of the produce should be washed well after washing hands. Pasteurized dairy products should be the type of dairy being consumed by the mother. [ 40 ]


Nutritional genomics , also known as  nutrigenomics , is a science studying the relationship between  human genome ,  human nutrition  and health.  People in the field work toward developing an understanding of how the whole body responds to a food via  systems biology , as well as single gene/single food compound relationships. [ 1 ] [ 2 ]  Nutritional genomics or Nutrigenomics is the relation between food and inherited genes, it was first expressed in 2001. [ 3 ] [ 4 ]


The term "nutritional genomics" is an umbrella term including several subcategories, such as nutrigenetics, nutrigenomics, and  nutritional epigenetics . Each of these subcategories explain some aspect of how genes react to nutrients and express specific phenotypes, like disease risk. [ 5 ]  There are several applications for nutritional genomics, for example how much nutritional intervention and therapy can successfully be used for disease prevention and treatment. [ 5 ]


Nutritional science originally emerged as a field that studied individuals lacking certain nutrients and the subsequent effects, [ 1 ]  such as the disease  scurvy  which results from a lack of vitamin C. As other diseases closely related to diet (but not deficiency), such as obesity, became more prevalent, nutritional science expanded to cover these topics as well. [ 1 ]  Nutritional research typically focuses on preventative measure, trying to identify what nutrients or foods will raise or lower risks of diseases and damage to the human body. [ 6 ] [ 7 ]


For example,  Prader–Willi syndrome , a disease whose most distinguishing factor is insatiable appetite, has been specifically linked to an epigenetic pattern in which the paternal copy in the chromosomal region is erroneously deleted, and the maternal loci is inactivated by over  methylation . [ 8 ]  Yet, although certain disorders may be linked to certain  single-nucleotide polymorphisms  (SNPs) or other localized patterns, variation within a population may yield many more polymorphisms. [ 9 ]


The  Mediterranean diet  refers to naturally occurring foods native to Greece, Italy Portugal and Spain before globalization of food products in the 20th century. The diet includes relatively high consumption of fruit, vegetables, olive oil, legumes, whole grains and moderate amounts of red wine. [ 10 ]  Foods with increased fat and dairy are minimally consumed. Some nutritional genomics studies have pointed towards the Mediterranean diet to be most nutritionally beneficial. It has been positively linked towards decreased mortality by providing protective agents against metabolic diseases, cardiovascular disease and several types of cancer. These benefits have been attributed to the abundance of dietary compounds present in Mediterranean staples. Examples of this include  Curcuma longa  (turmeric),  resveratrol ,  lycopene ,  capsaicin ,  quercetin , and  polyphenols  in extra virgin olive oil. It has been suggested that each of these allegedly bioactive compounds, along with several others, interacts with the body's cellular and molecular function, gene expression and epigenome to prevent  angiogenesis  and the development of  neurodegenerative disease . [ 11 ]


The applications of nutritional genomics are multiple. With personalized assessment some disorders ( diabetes , metabolic syndrome) can be identified. Nutrigenomics can help with personalized health and nutrition intake by assessing individuals and make specific nutritional requirements. [ 12 ]  The focus is in the prevention and the correction of specific genetic disorders. Examples of genetic related disorders that improve with nutritional correction are obesity,  coronary heart disease  (CHD), hypertension and diabetes mellitus type 1. [ 13 ]  Genetic disorders that can often be prevented by proper nutritional intake of parents include  spina bifida , alcoholism and  phenylketouria .


Genes tied to nutrition manifest themselves through the body's sensitivity to food. In studies about CHD, there is a relationship between the disease and the presence of two alleles found at E and B  apolipoprotein  loci. [ 14 ]  These loci differences result in individualized reactions to the consumption of lipids. Some people experience increased weight gain and greater risk of CHD whereas others with different loci do not. Research has shown a direct correlation between the decrease risk of CHD and the decrease consumption of lipids across all populations. [ 15 ]


Obesity is one of the most widely studied topics in nutritional genomics. Due to genetic variations among individuals, each person could respond to diet differently. By exploring the interaction between dietary pattern and genetic factors, the field aims to suggest dietary changes that could prevent or reduce obesity. [ 16 ]


There appear to be some SNPs that make it more likely that a person will gain weight from a high fat diet; for people with AA genotype in the  FTO gene  showed a higher BMI compared those with TT genotype when having high fat or low carbohydrate dietary intake. [ 16 ]  The  APO B  SNP rs512535 is another diet-related variation; the A/G heterozygous genotype was found to have association with obesity (in terms of BMI and waist circumference) and for individuals with habitual high fat diet (>35% of energy intake), while individuals with GG homozygous genotype are likely to have a higher BMI compared to AA allele carriers. However, this difference is not found in low fat consuming group (<35% of energy intake). [ 16 ]


Phenylketonuria, otherwise known as PKU, is an uncommon autosomal recessive metabolic disorder that takes effect postpartum but the debilitating symptoms can be reversed with nutritional intervention. [ 5 ]


Omics  is the collective characterization and quantification of entire sets of  biological molecules  and the investigation of how they translate into the structure, function, and dynamics of an  organism  or group of organisms. [ 1 ] [ 2 ]  The branches of  science  known informally as  omics  are various disciplines in  biology  whose names end in the suffix  -omics , such as  genomics ,  proteomics ,  metabolomics ,  metagenomics ,  phenomics  and  transcriptomics .


The related suffix  -ome  is used to address the objects of study of such fields, such as the  genome ,  proteome  or  metabolome  respectively. The suffix  -ome  as used in molecular biology refers to a  totality  of some sort; it is an example of a "neo-suffix" formed by abstraction from various Greek terms in   -ωμα , a sequence that does not form an identifiable suffix in Greek.


Functional genomics  aims at identifying the functions of as many genes as possible of a given organism. It combines
different -omics techniques such as transcriptomics and proteomics with saturated mutant collections. [ 3 ]


The  Oxford English Dictionary  ( OED ) distinguishes three different fields of application for the  -ome  suffix:


The  -ome  suffix originated as a variant of  -oma , and  became productive in the last quarter of the 19th century. It originally appeared in terms like  sclerome [ 4 ]  or  rhizome . [ 5 ]  All of these terms derive from Greek words in  -ωμα , [ 6 ]  a sequence that is  not a single suffix, but analyzable as   -ω-μα , the  -ω-  belonging to the word stem (usually a verb) and the  -μα  being a genuine Greek suffix forming abstract nouns.


The OED suggests that its third definition originated as a  back-formation  from  mitome , [ 7 ]   Early attestations include  biome  (1916) [ 8 ]  and  genome  (first coined as German  Genom  in 1920 [ 9 ] ). [ 10 ]


The association with  chromosome  in molecular biology is by  false etymology . The word  chromosome  derives from the  Greek   stems   χρωμ(ατ)-  "colour" and  σωμ(ατ)-  "body". [ 10 ]  While  σωμα  "body" genuinely contains the  -μα   suffix, the preceding   -ω-  is not a stem-forming suffix but part of the word's  root .  Because  genome  refers to the  complete  genetic makeup of an organism, a neo-suffix   -ome  suggested itself as referring to "wholeness" or "completion". [ 11 ]


Bioinformaticians  and  molecular biologists  figured amongst the first scientists to apply the "-ome" suffix widely. [ citation needed ]  Early advocates included bioinformaticians in  Cambridge , UK, where there were many early bioinformatics labs such as the  MRC centre ,  Sanger centre , and EBI ( European Bioinformatics Institute ); for example, the MRC centre carried out the  first genome  and proteome projects. [ 12 ]


Many "omes" beyond the original " genome " have become useful and have been widely adopted by research scientists. " Proteomics " has become well-established as a term for studying  proteins  at a large scale. "Omes" can provide an easy shorthand to encapsulate a field; for example, an  interactomics  study is clearly recognisable as relating to large-scale analyses of gene-gene, protein-protein, or protein-ligand interactions. Researchers are rapidly taking up omes and omics, as shown by the explosion of the use of these terms in  PubMed  since the mid-1990s. [ 13 ]


The  epigenome  is the supporting structure of the genome, including protein and RNA binders, alternative DNA structures, and chemical modifications on DNA.


The  microbiome  is a microbial community occupying a well-defined habitat with distinct physio-chemical properties. It includes the microorganisms involved and their theatre of activity, forming  ecological niches.  Microbiomes form dynamic and interactive micro-ecosystems prone to spaciotemporal change. They are integrated into macro-ecosystems, such as eukaryotic hosts, and are crucial to the host's proper function and health. [ 18 ]  The interactive host-microbe systems make up the  holobiont . [ 19 ]


Microbiomics is the study of microbiome dynamics, function, and structure. [ 20 ]  This area of study employs several techniques to study the microbiome in its host environment: [ 19 ]


The  lipidome  is the entire complement of cellular  lipids , including the modifications made to a particular set of lipids, produced by an organism or system.


The  proteome  is the entire complement of  proteins , including the modifications made to a particular set of proteins, produced by an organism or system.


Glycomics  is the comprehensive study of the  glycome  i.e. sugars and carbohydrates.


Foodomics  was defined by  Alejandro Cifuentes  in 2009 as "a discipline that studies the food and nutrition domains through the application and integration of advanced omics technologies to improve consumer’s well-being, health, and knowledge." [ 23 ] [ 24 ]


Transcriptome  is the set of all  RNA  molecules, including mRNA, rRNA, tRNA, and other non-coding RNA, produced in one or a population of cells.


The  metabolome  is the ensemble of small molecules found within a biological matrix.


Inspired by foundational questions in evolutionary biology, a Harvard team around Jean-Baptiste Michel and  Erez Lieberman Aiden  created the American neologism  culturomics  for the application of big data collection and analysis to  cultural studies . [ 25 ]


The word "comic" does not use the "omics" suffix; it derives from Greek "κωμ(ο)-" ( merriment ) + "-ικ(ο)-" (an adjectival suffix), rather than presenting a truncation of "σωμ(ατ)-".


Similarly, the word "economy" is assembled from Greek "οικ(ο)-" ( household ) + "νομ(ο)-" ( law  or  custom ), and "economic(s)" from "οικ(ο)-" + "νομ(ο)-" + "-ικ(ο)-". The suffix -omics is sometimes used to create names for schools of  economics , such as  Reaganomics .


An  operational taxonomic unit  ( OTU ) is an operational definition used to classify groups of closely related individuals. The term was originally introduced in 1963 by  Robert R. Sokal  and  Peter H. A. Sneath  in the context of  numerical taxonomy , where an "operational taxonomic unit" is simply the group of organisms currently being studied. [ 1 ]  Numerical taxonomy is a method in biological systematics that involves using numerical techniques to classify taxonomic units based on the states of their characteristics. [ 2 ]  In this sense, an OTU is a pragmatic definition to group individuals by similarity, equivalent to but not necessarily in line with classical  Linnaean taxonomy  or modern  evolutionary taxonomy .


OTUs are employed in microbial community DNA sequencing research to delineate species-level distinctions among organisms and represent the most frequently utilized unit for measuring microbial diversity. [ 3 ]  Nowadays, however, the term "OTU" is commonly used in a different context and refers to clusters of (uncultivated or unknown) organisms, grouped by DNA sequence similarity of a specific taxonomic marker gene (originally coined as mOTU; molecular OTU). [ 4 ]  In other words, OTUs are pragmatic proxies for " species " (microbial or  metazoan ) at different taxonomic levels, in the absence of traditional systems of  biological classification  as are available for macroscopic organisms. For several years, OTUs have been the most commonly used units of diversity, especially when analysing small subunit  16S  (for prokaryotes) or  18S rRNA  (for eukaryotes [ 5 ] ) marker gene sequence datasets.


Sequences can be clustered according to their similarity to one another, and operational taxonomic units are defined based on the similarity threshold (usually 97% similarity; however also 100% similarity is common, also known as  single variants [ 6 ] ) set by the researcher.  It remains debatable how well this commonly-used method recapitulates true microbial species phylogeny or ecology. Although OTUs can be calculated differently when using different algorithms or thresholds, research by Schmidt et al. (2014) demonstrated that microbial OTUs were generally ecologically consistent across habitats and several OTU clustering approaches. [ 7 ]  The number of OTUs defined may be inflated due to errors in DNA sequencing. [ 8 ]


There are three main approaches to clustering OTUs: [ 9 ]


In  molecular genetics , an  ORFeome  refers to the complete set of  open reading frames  (ORFs) in a  genome . The term may also be used to describe a set of  cloned  ORFs. [ 1 ]  ORFs correspond to the protein coding sequences (CDS) of  genes . ORFs can be found in genome sequences by computer programs such as  GENSCAN  and then amplified by  PCR . While this is relatively trivial in  bacteria  the problem is non-trivial in  eukaryotic   genomes  because of the presence of  introns  and  exons  as well as  splice variants .


The usage of complete ORFeomes reflects a new trend in biology that can be succinctly summarized as  omics . ORFeomes are used for the study of protein-protein interactions, [ 2 ] [ 3 ]   protein microarrays , the study of antigens, [ 4 ]  and other fields of study.


Complete ORF sets have been cloned for a number of organisms including  Brucella melitensis , [ 5 ]
 Chlamydia pneumoniae , [ 6 ]
 Escherichia coli , [ 7 ]
 Neisseria gonorrhoeae , [ 8 ]
 Pseudomonas aeruginosa , [ 9 ]   Schizosaccharomyces pombe ,  Staphylococcus aureus [ 10 ]
and human  herpesviruses [ 11 ]


A partial human ORFeome has also been produced. [ 12 ] [ 13 ]


Orphan genes ,  ORF ans, [ 1 ] [ 2 ]  or  taxonomically restricted genes (TRGs) [ 3 ]  are  genes  that lack a detectable  homologue  outside of a given  species  or lineage. [ 2 ]  Most genes have known homologues. Two genes are homologous when they share an evolutionary history, and the study of groups of homologous genes allows for an understanding of their evolutionary history and divergence. Common mechanisms that have been uncovered as sources for new genes through studies of homologues include  gene duplication ,  exon shuffling ,  gene fusion  and fission, etc. [ 4 ] [ 5 ]  Studying the origins of a gene becomes more difficult when there is no evident homologue. [ 6 ]  The discovery that about 10% or more of the genes of the average microbial species is constituted by orphan genes raises questions about the evolutionary origins of different species as well as how to study and uncover the evolutionary origins of orphan genes.


In some cases, a gene can be classified as an orphan gene due to undersampling of the existing genome space. While it is possible that homologues exist for a given gene, that gene will still be classified as an orphan if the organisms harbouring homologues have not yet been discovered and had their genomes  sequenced  and properly  annotated . For example, one study of orphan genes across 119  archaeal  and  bacterial  genomes could identify that at least 56% were recently acquired from  integrative elements  (or  mobile genetic elements ) from non-cellular sources such as  viruses  and  plasmids  that remain to be explored and characterized, and another 7% arise through  horizontal gene transfer  from distant cellular sources (with an unknown proportion of the remaining 37% potentially coming from still unknown families of integrative elements). [ 7 ]  In other cases, limitations in computational methods for detecting homologues may result in missed homologous sequences and thus classification of a gene as an orphan. Homology detection failure appears to account for the majority, but not all orphan genes. [ 8 ]  In other cases, homology between genes may go undetected due to rapid evolution and divergence of one or both of these genes from each other to the point where they do not meet the criteria used to classify genes as evidently homologous by computational methods. One analysis suggests that divergence accounts for a third of orphan gene identifications in eukaryotes. [ 9 ]  When homologous genes exist but are simply undetected, the emergence of these orphan genes can be explained by well-characterized phenomena such as genomic recombination, exon shuffling, gene duplication and divergence, etc. Orphan genes may also simply lack true homologues and in such cases have an independent origins via  de novo gene birth , which tends to be a more recent event. [ 2 ]  These processes may act at different rates in insects, primates, and plants. [ 10 ]  Despite their relatively recent origin, orphan genes may encode functionally important proteins. [ 11 ] [ 12 ]  Characteristics of orphan genes include AT richness, relatively recent origins, taxonomic restriction to a single genome, elevated evolution rates, and shorter sequences. [ 13 ]


Some approaches characterize all microbial genes as part of one of two classes of genes. One class is characterized by conservation or partial conservation across lineages, whereas the other (represented by orphan genes) is characterized by evolutionarily instantaneous rates of gene turnover/replacement with a negligible effect on fitness when such genes are either gained or lost. These orphan genes primarily derive from mobile genetic elements and tend to be 'passively selfish', often devoid of cellular functions (which is why they experience little selective pressure in their gain or loss from genomes) but persist in the biosphere due to their transient movement across genomes. [ 14 ] [ 15 ]


Orphan genes  evolve more rapidly  than other genes. They often originate through two primary mechanisms:  de novo gene birth , where new genes emerge from non-coding sequences within the genome, and  horizontal gene transfer , the acquisition of genetic material from another organism.


Biologists believe orphan genes may play a crucial role in developing species-specific traits, environmental adaptations, or responses to changing ecological niches. These functional innovations necessitate rapid evolutionary changes to optimize their efficacy within the organism's biology.


Multiple studies have supported these evolutionary theories regarding orphan genes. Domazet-Loso and Tautz [ 16 ]  conducted a study focusing on orphan genes in Drosophila, revealing that these genes evolve at a faster pace compared to conserved genes. This finding suggests a potential correlation between evolutionary rate and gene novelty. Similarly, Tautz and Domazet-Loso [ 17 ]  presented evidence indicating a substantial contribution of orphan genes to phenotypic diversity and adaptation across different species. Their research underscores the crucial role of orphan genes in driving evolutionary innovation and shaping biological diversity.


Orphan genes were first discovered when the yeast genome-sequencing project began in 1996. [ 2 ]  Orphan genes accounted for an estimated 26% of the yeast genome, but it was believed that these genes could be classified with homologues when more genomes were sequenced. [ 3 ]  At the time, gene duplication was considered the only serious model of gene evolution [ 2 ] [ 4 ] [ 18 ]  and there were few sequenced genomes for comparison, so a lack of detectable homologues was thought to be most likely due to a lack of sequencing data and not due to a true lack of homology. [ 3 ]  However, orphan genes continued to persist as the quantity of sequenced genomes grew, [ 3 ] [ 19 ]  eventually leading to the conclusion that orphan genes are ubiquitous to all genomes. [ 2 ]  Estimates of the percentage of genes which are orphans varies enormously between species and between studies; 10-30% is a commonly cited figure. [ 3 ]


The study of orphan genes emerged largely after the turn of the century. In 2003, a study of  Caenorhabditis briggsae  and related species compared over 2000 genes. [ 3 ]  They proposed that these genes must be evolving too quickly to be detected and are consequently sites of very rapid evolution. [ 3 ]   In 2005, Wilson examined 122 bacterial species to try to examine whether the large number of orphan genes in many species was legitimate. [ 19 ]  The study found that it was legitimate and played a role in bacterial adaptation. The definition of taxonomically-restricted genes was introduced into the literature to make orphan genes seem less "mysterious." [ 19 ]


In 2008, a yeast protein of established functionality, BSC4, was found to have evolved de novo from non-coding sequences whose homology was still detectable in sister species. [ 20 ]


In 2009, an orphan gene was discovered to regulate an internal biological network:  the orphan gene, QQS, from  Arabidopsis thaliana  modifies plant composition. [ 21 ]   The QQS orphan protein interacts with a conserved transcription factor, these data explain the compositional changes (increased protein) that are induced when QQS is engineered into diverse species. [ 22 ]  In 2011, a comprehensive genome-wide study of the extent and evolutionary origins of orphan genes in plants was conducted in the model plant  Arabidopsis thaliana  " [ 23 ]


Genes can be tentatively classified as orphans if no orthologous proteins can be found in nearby species. [ 10 ]


One method used to estimate nucleotide or protein sequence similarity indicative of homology (i.e. similarity due to common origin) is the  Basic Local Alignment Search Tool  (BLAST). BLAST allows query sequences to be rapidly searched against large sequence databases. [ 24 ] [ 25 ]  Simulations suggest that under certain conditions BLAST is suitable for detecting distant relatives of a gene. [ 26 ]  However, genes that are short and evolve rapidly can easily be missed by BLAST. [ 27 ]


The systematic detection of homology to annotate orphan genes is called phylostratigraphy. [ 28 ]  Phylostratigraphy generates a phylogenetic tree in which the homology is calculated between all genes of a focal species and the genes of other species. The earliest common ancestor for a gene determines the age, or  phylostratum , of the gene. The term "orphan" is sometimes used only for the youngest phylostratum containing only a single species, but when interpreted broadly as a taxonomically-restricted gene, it can refer to all but the oldest phylostratum, with the gene orphaned within a larger clade.


Homology detection failure accounts for a majority of classified orphan genes. [ 8 ]  Some scientists have attempted to recover some homology by using more sensitive methods, such as remote homology detection. In one study, remote homology detection techniques were used to demonstrate that a sizable fraction of orphan genes (over 15%) still exhibited remote homology despite being missed by conventional homology detection techniques, and that their functions were often related to the functions of nearby genes at genomic loci. [ 29 ]


Orphan genes arise from multiple sources, predominantly through  de novo  origination, duplication and rapid divergence, and horizontal gene transfer. [ 2 ]


Novel orphan genes continually arise  de novo  from non-coding sequences. [ 30 ]  These novel genes may be sufficiently beneficial to be swept to fixation by selection. Or, more likely, they will fade back into the non-genic background. This latter option is supported by research in Drosophila showing that young genes are more likely go extinct. [ 31 ]


De novo  genes were once thought to be a near impossibility due to the complex and potentially fragile intricacies of creating and maintaining functional polypeptides, [ 18 ]  but research from the past 10 years or so has found multiple examples of  de novo  genes, some of which are associated with important biological processes, particularly testes function in animals.  De novo  genes were also found in fungi and plants. [ 20 ] [ 32 ] [ 33 ] [ 5 ] [ 34 ] [ 35 ] [ 11 ] [ 36 ]


For young orphan genes, it is sometimes possible to find homologous  non-coding DNA  sequences in sister taxa, which is generally accepted as strong evidence of  de novo  origin. However, the contribution of  de novo  origination to taxonomically-restricted genes of older origin, particularly in relation to the traditional gene duplication theory of gene evolution, remains contested. [ 37 ] [ 38 ]  Logistically, de novo origination is much easier for RNA genes than protein-coding ones and  Nathan H. Lents  and colleagues recently reported the existence of several young  microRNA  genes on  human chromosome 21 . [ 39 ]


The duplication and divergence model for orphan genes involves a new gene being created from some duplication or divergence event and undergoing a period of rapid evolution where all detectable similarity to the originally duplicated gene is lost. [ 2 ]  While this explanation is consistent with current understandings of duplication mechanisms, [ 2 ]  the number of mutations needed to lose detectable similarity is large enough as to be a rare event, [ 2 ] [ 26 ]  and the evolutionary mechanism by which a gene duplicate could be sequestered and diverge so rapidly remains unclear. [ 2 ] [ 40 ]


Another explanation for how orphan genes arise is through a duplication mechanism called  horizontal gene transfer , where the original duplicated gene derives from a separate, unknown lineage. [ 2 ]  This explanation for the origin of orphan genes is especially relevant in bacteria and archaea, where horizontal gene transfer is common.


Orphans genes tend to be very short (~6 times shorter than mature genes), and some are weakly expressed, tissue specific and simpler in codon usage and amino acid composition. [ 41 ]  Orphan genes tend to encode more  intrinsically disordered proteins , [ 42 ] [ 43 ] [ 44 ]  although some structure has been found in one of the best characterized orphan genes. [ 45 ]
Of the tens of thousands of enzymes of primary or specialized metabolism that have been characterized to date, none are orphans, or even of restricted lineage; apparently, catalysis requires hundreds of millions of years of evolution. [ 41 ]


Orphan genes, which have no detectable  homologs  in other species, represent a fascinating area of study in genomics. Their evolutionary role and biological significance remain subjects of ongoing research and debate. Orphan genes are important in evolution and speciation because of the potential for the production of novel genes and functions. [ 46 ]  Orphan genes are theorized to play a critical role in the evolution of species, as they allow organisms to respond to changes in their environment and develop new adaptations rapidly. [ 47 ]


Orphan genes can have diverse functions, ranging from basic metabolic functions to complex regulatory processes. For example, some orphan genes are involved in the regulation of growth and development, while others play a role in the response to the environmental stresses. [ 48 ]  Their evolutionary role and biological significance remain subjects of ongoing research and debate.


Some scientists propose that many orphan genes may not play a direct evolutionary role. They argue that genomes contain non-functional  open reading frames  (ORFs) which might produce spurious polypeptides not maintained by natural selection. Such genes are likely to be unique to a species because they do not undergo conservation across species, hence are categorized as orphan genes. [ 49 ]


Contrary to the view that they are evolutionary noise, emerging studies have illustrated the functional importance of orphan genes:


These examples confirm the functionality of some orphan genes but also suggest their potential involvement in the emergence of novel phenotypes, thereby contributing to species-specific adaptations.


Orphan genes have garnered interest across multiple scientific disciplines such as evolutionary biology and medicine, due to their nature and potential implications. [ 52 ]


In  evolutionary biology , orphan genes diverge from traditional models of gene evolution and provide valuable insights into the process of de novo gene origination and lineage-specific adaptation. The term "de novo gene" specifically denotes the emergence of a functional gene without ancestral genetic material, whether as a protein-coding gene or a functional  RNA  molecule. [ 53 ]  This understanding of de novo genes, coupled with the study of orphan genes, enriches the traditional  Charles Darwin 's model of evolution, also called  Darwinism  or Darwinian theory, by revealing additional mechanisms through which genetic diversity and adaptation can occur. By clarifying that de novo genes can arise from non-genic sequences and contribute to lineage-specific adaptation, this research expands our understanding of the creative forces of evolution, adding depth and complexity to Darwin's foundational principles.


In  medicine , orphan genes represent a rich yet relatively unexplored resource that holds promise for understanding human health and addressing disease. These genes, which lack detectable homologs in other lineages, offer unique opportunities for biomedical research. [ 52 ]  By elucidating the functions and regulatory mechanisms of orphan genes, researchers can gain insights into various aspects of human health. Orphan genes may play crucial roles in diseases that are poorly understood or have unknown genetic origins. Studying these genes can uncover novel disease mechanisms and therapeutic targets, paving the way for the development of innovative treatment strategies. To name a few, the orphan gene  Gpr49 , identified in humans, presents itself as a potential novel therapeutic target in combating  hepatocellular carcinoma , the predominant form of liver  cancer . [ 52 ]  Furthermore, the gene  C19orf12  is implicated in the manifestation of a particular clinical subtype of  neurodegeneration  characterized by brain iron accumulation. [ 52 ]  An excerpt from a table listing various orphan genes across diverse species along with their respective functions is shown. [ 52 ]


Orphan genes have the potential to serve as biomarkers for disease diagnosis, prognosis, and treatment response. Their lineage-specific nature and expression patterns may provide valuable information for personalized medicine approaches, enabling more accurate and targeted interventions for individuals affected by various diseases. Thus, harnessing the potential of orphan genes in understanding human health has significant implications for advancing biomedical research and improving clinical outcomes.


Paleogenomics  is a field of science based on the reconstruction and analysis of  genomic  information in extinct  species . Improved methods for the extraction of  ancient DNA  (aDNA) from museum artifacts,  ice cores , archeological or paleontological sites, and  next-generation sequencing  technologies have spurred this field. It is now possible to detect  genetic drift , ancient population migration and interrelationships, the evolutionary history of extinct plant, animal and  Homo  species, and identification of phenotypic features across geographic regions. Scientists can also use paleogenomics to compare ancient ancestors against modern-day humans. [ 1 ]  The rising importance of  paleogenomics is evident from the fact that the 2022 Nobel Prize in physiology or medicine was awarded to a Swedish geneticist  Svante Pääbo  [1955-], who worked on paleogenomics.


Initially, aDNA sequencing involved cloning small fragments into bacteria, which proceeded with low efficiency due to the oxidative damage the aDNA suffered over millennia. [ 2 ]  aDNA is difficult to analyze due to facile degradation by  nucleases ; specific environments and postmortem conditions improved isolation and analysis. Extraction and  contamination  protocols were necessary for reliable analyses. [ 3 ]  With the development of the Polymerase Chain Reaction ( PCR ) in 1983, scientists could study DNA samples up to approximately 100,000 years old, a limitation of the relatively short isolated fragments. Through advances in isolation, amplification, sequencing, and data reconstruction, older and older samples have become analyzable. Over the past 30 years, high copy number  mitochondrial DNA  was able to answer many questions; the advent of  NGS  techniques prompted far more. Moreover, this technological revolution allowed the transition from  paleogenetics  to paleogenomics. [ 1 ]


PCR ,  NGS  second generation, and various library methods are available for sequencing aDNA, besides many  bioinformatics  tools. When dealing with each of these methods it is important to consider that aDNA can be altered post-mortem. [ 2 ]  Specific alterations arise from:


Specific patterns and onset of these alterations help scientists to estimate the sample's age.


Formerly, scientists diagnosed post-mortem damages using enzymatic reactions or  gas chromatography  associated with  mass spectroscopy ; in more recent years scientists began to detect them by exploiting mutational sequence data. This strategy allows to identify excess of C->T mutations following treatment with  uracil   DNA glycosylase . Nowadays, one uses  high-throughput sequencing (HTS)  to identify depurination (a process that drives post-mortem DNA fragmentation, younger samples present more  adenine  than  guanine ),  single strand breaks  in double helix of DNA and abasic site (created by C->T mutation).
 A single fragment of aDNA can be sequenced in its full length with HTS. With these data we can create a distribution representing a size decay curve that enables a direct quantitative comparison of fragmentation across specimens through space and environmental conditions. Throughout the decay curve it is possible to obtain the median length of the given fragment of aDNA. This length reflects the fragmentation levels after death, which generally increases with depositional temperature. [ 4 ]


Two different libraries can be performed for aDNA sequencing using  PCR  for  genome  amplification:


The first one is created using the blunt-end approach. This technique uses two different adaptors: these adaptors bind randomly the fragment and it can then be amplified. The fragment that does not contain both adaptors cannot be amplified causing an error source. To reduce this error,  Illumina  T/A ligation was introduced: this method consists in inserting the A tailing in DNA sample to facilitate the ligation of T tailed adaptors. In this methods we optimize the amplification of the aDNA.


To obtain ssDNA libraries, DNA is first  denatured  with heat. The obtained ssDNA is then ligated to two adaptors in order to generate the  complementary strand  and finally  PCR  is applied. [ 4 ]


As aDNA may contain bacterial DNA or other microorganisms, the process requires enrichment. In order to separate endogenous and exogenous fractions, various methods are employed:


By now many studies in different fields have led to the conclusion that present-day non-African population is the result of the diversification in several different  lineages  of an ancestral, well-structured,  metapopulation  which was the protagonist of an out-of-Africa expansion, in which it carried a subset of African  genetic heritage . In this context, the analysis of ancient DNA was fundamental to test already formulated hypothesis and to provide new insights. First, it has allowed to narrow the timing and the structure of this diversification phenomenon by providing the calibration of the autosomal and mitochondrial  mutation rate . [ 7 ]   Admixture  analysis has demonstrated that at least two independent  gene flow  events have occurred between ancestors of  modern humans  and archaic humans, such as  Neanderthal  and  Denisovan  populations, testifying the “leaky replacement” model of Eurasian human population history. According to all these data, the human divergence of the non-African lineages occurred around 45,000 – 55,000  BP . [ 7 ]  Besides that, in many cases ancient DNA has allowed to track historical processes which have led, in time, to the actual population genetic structure, which would have been difficult to do counting only on the analysis of present-day genomes. Among these still unresolved questions, some of the most studied are the identity of the first inhabitants of the Americas, the peopling  of Europe and the origin of agriculture in Europe. [ 1 ]


Analysis of  ancient DNA  allows to study mutations of  phenotypic traits  following changes in environment and human behavior. Migration to new habitats, new dietary shifts (following the transition to agriculture) and building of large communities led to the exposure of humans to new conditions that ultimately resulted in  biological adaptation .


Migration of humans out of  Africa  to higher latitudes involved less exposure to sunlight. Since  UVA  and  UVB  rays are crucial for the synthesis of  vitamin D , which regulates calcium absorption and thus is essential for bone health, living at higher latitudes would mean a substantial reduction in  vitamin D  synthesis. This put a new  selective pressure  on skin colour trait, favouring lighter skin colour at higher latitudes.
The two most important genes involved in skin pigmentation are SLC24A5 and SLC45A2. Nowadays the “light skin” alleles of these genes are fixed in  Europe  but they reached a relatively high frequency only fairly recently (about 5000 years ago). [ 7 ]  Such slow depigmentation process suggests that ancient Europeans could have faced the downsides of low vitamin D production, such as musculoskeletal and cardiovascular conditions. Another hypothesis is that pre-agricultural Europeans could have met their vitamin D requirements through their diet (since meat and fish contain some vitamin D) [ 8 ]


One of the major examples of adaptation following the switch to agricultural diet is the persistence of production of the  lactase  enzyme in adulthood. This enzyme is essential to digest  lactose  present in milk and dietary products and its absence leads to diarrhea following the consumption of these products.  Lactase  persistence is determined predominantly by a single-base mutation in the MCM6 gene and ancient DNA data show that this mutation became common only within the past 5000 years, thousands of years after the beginning of dairying practices. [ 7 ]  Thus, even in the case of lactase-persistence there is a huge time delay between the onset of a new habit and the spread of the adaptive allele and so milk consumption may have been restricted to children or to lactose-reduced products.


Another example of mutation positively selected by the switch to agriculture is the number of AMY1 gene copies. AMY1 encodes for the starch-digesting enzyme  amylase  present in saliva and modern humans have a higher number of gene copies compared to  chimpanzees . [ 8 ]


The human  immune system  has undergone intense selection through the millennia, adapting to different pathogen landscapes. Several environmental and cultural changes have imposed a  selective pressure  on different immune-associated genes. Migrations, for example, exposed humans to new habitats carrying new pathogens or pathogen vectors (e.g. mosquitos). Also the switch to agriculture involved exposition to different pathogens and health conditions, both due to the increased population density and to living close to livestock.
However, it is difficult to directly correlate particular ancient genome changes to improved resistance to particular pathogens, giving the vastness and complexity of the human immune system.
Besides studying directly changes in the human immune system, it is also possible to study the ancient genomes of pathogens, such as those causing  tuberculosis ,  leprosy ,  plague ,  smallpox  or  malaria . For example, researchers have discovered that all strains of  Yersinia pestis  before 3600 years ago were lacking the  ymt  gene, which is essential for the pathogen to survive in the intestine of  fleas . [ 8 ]  This suggests that in the ancient past plague may had been less virulent compared to more recent  Y. pestis  outbreaks.


A study of  ancient DNA  supported or confirmed [ 9 ]  that  recent human evolution  to resist infection of  pathogens  also increased  inflammatory disease  risk in post-Neolithic Europeans over the last 10,000 years, estimating nature, strength, and time of onset of selections due to pathogens. [ 10 ]


Many non-hominin  vertebrates  - ancient  mammoth ,  polar bear ,  dog  and  horse  - have been reconstructed through aDNA recovery from  fossils  and samples preserved at low temperature or high altitude. Mammoth studies are most frequent due to the high presence of soft tissue and hair from permafrost and are used to identify the relationship and demographic changes with more recent  elephants . Polar bear studies are performed to identify the impact of  climate change  in  evolution  and  biodiversity . Dog and horse studies give insights into  domestication . In plants, aDNA has been isolated from  seeds ,  pollen  and  wood . A correlation has been identified between ancient and extant  barley . Another application was the detection of domestication and adaptation process of  maize  which include genes for  drought  tolerance and  sugar  content. [ 1 ]


The analysis of ancient genomes of anatomically modern humans has, in recent years, completely revolutionized our way of studying population migrations, transformation and evolution. Nevertheless, much still remains unknown. The first and obvious problem related to this kind of approach, which is going to be partially overcome by the continuous improvement of the ancient DNA extraction techniques, is the difficulty of recovering well preserved ancient genomes, a challenge that is particularly observed in Africa and in Asia, where the temperatures are higher than in other colder regions of the world. Further, Africa is, among all the continents, the one that harbors the most  genetic diversity . [ 7 ]  Besides DNA degradation, also exogenous contamination limits paleogenomic sequencing and assembly processes. [ 1 ]  As we do not possess ancient DNA coming from the time and the region inhabited by the original ancestors of present-day non-African population, we still know little about their structure and location. The second and more important challenge that this matter has to face is the recovery of DNA from early modern humans (100,000 – 200,000 BP). These data, together with a major number of archaic genomes to analyze and with the knowledge of the timing and of the distribution of archaic genetic admixture, will allow scientists to more easily reconstruct the history of our species. In fact, collecting more data about or genetic history will allow us to track human evolution not only in terms of migrations and  natural selection , but also in terms of culture. In the next decade paleogenomics research field is going to focus its attention mainly on three topics: the definition, at a fine-scale detail, of past human interactions by denser sampling, the comprehension of how these interactions have contributed to agricultural transition by analysis of DNA of understudied regions and, finally, the quantification of the natural selection contribution to present-day phenotypes. To interpret all these data geneticists will be required to cooperate, as they have already done with  anthropologists  and  archaeologists , with  historians . [ 7 ]


Bioethics  in paleogenomics concerns ethical questions that arise in the study of ancient human remains, due to the complex relationships among scientists, governments and indigenous  populations . In addition, paleogenomic studies have the potential to harm community or individual histories and identities, as well as to reveal denouncing information about their descendants. For these reasons, these kind of studies are still a touchy subject.
Paleogenomics studies can have negative consequences mainly because of the discrepancies between articulations of ethical principles and practices. In fact, ancestors’ remains are usually considered legally and scientifically as “artifacts”, rather than “human subjects”, which justifies questionable behaviors and lack of engagement from  communities . Testing of ancestral remains are therefore used in disputes, claims in treaty, repatriation, or other legal cases.
The acknowledgement of the importance and susceptibility of this subject is heading towards ethical commitment and guidance applicable to different contexts, in order to preserve ancestral remains’ dignity and avoid ethical issues. [ 11 ]  Finally, another pioneering area of interest is the so-called “de-extinction” project, which aims to the resurrection of extinct species, such as the mammoth. This project, which appears to be possible thanks to the  CRISPR/Cas9  technology, is, however, strongly connected to many ethical issues. [ 1 ]


In the fields of  molecular biology  and  genetics , a  pan-genome  ( pangenome  or  supragenome ) is the entire set of  genes  from all  strains  within a  clade . More generally, it is the  union  of all the genomes of a clade. [ 2 ] [ 3 ] [ 4 ] [ 5 ]  The pan-genome can be broken down into a "core pangenome" that contains genes present in all individuals, a "shell pangenome" that contains genes present in two or more strains, and a "cloud pangenome" that contains genes only found in a single strain. [ 3 ] [ 4 ] [ 6 ]  Some authors also refer to the cloud genome as "accessory genome" containing 'dispensable' genes present in a subset of the strains and strain-specific genes. [ 2 ] [ 3 ] [ 4 ]  Note that the use of the term 'dispensable' has been questioned, at least in plant genomes, as accessory genes play "an important role in genome evolution and in the complex interplay between the genome and the environment". [ 5 ]  The field of study of pangenomes is called pangenomics. [ 2 ]


The genetic repertoire of a bacterial species is much larger than the gene content of an individual strain. [ 7 ]  Some  species  have open (or extensive) pangenomes, while others have closed pangenomes. [ 2 ]  For species with a closed pan-genome, very few genes are added per sequenced genome (after sequencing many strains), and the size of the full pangenome can be theoretically predicted. Species with an open pangenome have enough genes added per additional sequenced genome that predicting the size of the full pangenome is impossible. [ 4 ]  Population size and  niche  versatility have been suggested as the most influential factors in determining pan-genome size. [ 2 ]


Pangenomes were originally constructed for species of  bacteria  and  archaea , but more recently  eukaryotic  pan-genomes have been developed, particularly for  plant  species. Plant studies have shown that pan-genome dynamics are linked to transposable elements. [ 8 ] [ 9 ] [ 10 ] [ 11 ]  The significance of the pan-genome arises in an evolutionary context, especially with relevance to  metagenomics , [ 12 ]  but is also used in a broader  genomics  context. [ 13 ]  An open access book reviewing the pangenome concept and its implications, edited by Tettelin and Medini, was published in the spring of 2020. [ 14 ]


The term 'pangenome' was defined with its current meaning by Tettelin et al. in 2005; [ 2 ]  it derives 'pan' from the Greek word  παν , meaning 'whole' or 'everything', while the  genome  is a commonly used term to describe an organism's complete genetic material. Tettelin et al. applied the term specifically to  bacteria , whose pangenome "includes a core genome containing genes present in all strains and a dispensable genome composed of genes absent from one or more strains and genes that are unique to each strain." [ 2 ]


Is the part of the pangenome that is shared by every  genome  in the tested set. Some authors have divided the core pangenome in hard core, those families of homologous genes that has at least one copy of the family shared by every genome (100% of genomes) and the soft core or extended core, [ 15 ]  those families distributed above a certain threshold (90%). In a study that involves the pangenomes of  Bacillus cereus  and  Staphylococcus aureus ,  some of them isolated from the international space station, the thresholds used for segmenting the pangenomes were as follows: "Cloud", "Shell", and "Core" corresponding to gene families with presence in <10%, 10–95%, and >95% of the genomes, respectively. [ 16 ]


The core genome size and proportion to the pangenome depends on several factors, but it is especially dependent on the phylogenetic similarity of the considered genomes. For example, the core of two identical genomes would also be the complete pangenome. The core of a genus will always be smaller than the core genome of a species. Genes that belong to the core genome are often related to  house keeping  functions and  primary metabolism  of the lineage, nevertheless, the core gene can also contain some genes that differentiate the species from other species of the genus, i.e. that may be related pathogenicity to niche adaptation. [ 17 ]


Is the part of the pangenome shared by the majority of the genomes in a pangenome. [ 18 ]  There is not a universally accepted threshold to define the shell genome, some authors consider a gene family as part of the shell pangenome if it shared by more than 50% of the genomes in the pangenome. [ 19 ]  A family can be part of the shell by several evolutive dynamics, for example by gene loss in a lineage where it was previously part of the core genome, such is the case of enzymes in the  tryptophan   operon  in  Actinomyces , [ 20 ]  or by gene gain and fixation of a gene family that was previously part of the dispensable genome such is the case of  trpF  gene in several  Corynebacterium  species. [ 21 ]


The cloud genome consists of those gene families shared by a minimal subset of the genomes in the pangenome, [ 22 ]  it includes singletons or genes present in only one of the genomes. It is also known as the peripheral genome, or accessory genome. Gene families in this category are often related to ecological adaptation. [ citation needed ]


The pan-genome can be somewhat arbitrarily classified as open or closed based on the alpha value of  Heaps' law :

 N
 =
 k

 n

 −
 α

 {\displaystyle N=kn^{-\alpha }}

   [ 23 ] [ 15 ]


Usually, the pangenome software can calculate the parameters of the Heap law that best describe the behavior of the data.


An open pangenome occurs when the number of new gene families in one taxonomic lineage keeps increasing without appearing to be  asymptotic  regardless how many new  genomes  are added to the pangenome.  Escherichia coli  is an example of a species with an open pangenome. Any  E. coli  genome size is in the range of 4000–5000 genes and the pangenome size estimated for this species with approximately 2000 genomes is composed by 89,000 different gene families. [ 24 ]  The pangenome of the domain  bacteria  is also considered to be open.


A closed pangenome occurs in a lineage when only few gene families are added when new  genomes  are incorporated into the pangenome analysis, and the total amount of gene families in the pangenome seem to be  asymptotic  to one number. It is believed that  parasitism  and species that are specialists in some ecological  niche  tend to have closed pangenomes.  Staphylococcus lugdunensis  is an example of a  commensal  bacteria with closed pan-genome. [ 25 ]


The original pangenome concept was developed by Tettelin et al. [ 2 ]  when they analyzed the  genomes  of eight isolates of  Streptococcus agalactiae ,  where they described a core genome shared by all isolates, accounting for approximately 80% of any single genome, plus a dispensable genome consisting of partially shared and strain-specific genes. Extrapolation suggested that the gene reservoir in the  S. agalactiae  pan-genome is vast and that new unique genes would continue to be identified even after sequencing hundreds of genomes. [ 2 ]  The pangenome comprises the entirety of the genes discovered in the sequenced genomes of a given microbial species and it can change when new genomes are sequenced and incorporated into the analysis. [ citation needed ]


The pangenome of a genomic lineage accounts for the intra lineage gene content variability. Pangenome evolves due to: gene duplication, gene gain and loss dynamics and interaction of the genome with mobile elements that are shaped by selection and drift. [ 26 ]  Some studies point that prokaryotes pangenomes are the result of adaptive, not  neutral evolution  that confer species the ability to migrate to new niches. [ 27 ]


The supergenome can be thought of as the real pangenome size if all genomes from a species were sequenced. [ 28 ]  It is defined as all genes accessible for being gained by a certain species. It cannot be calculated directly but its size can be estimated by the pangenome size calculated from the available genome data. Estimating the size of the cloud genome can be troubling because of its dependence on the occurrence of rare genes and genomes. In 2011 genomic fluidity was proposed as a measure to categorize the gene-level similarity among groups of sequenced isolates. [ 29 ]  In some lineages the supergenomes did appear  infinite , [ 30 ]  as is the case of the Bacteria domain. [ 31 ]


'Metapangenome' has been defined as the outcome of the analysis of pangenomes in conjunction with the environment where the abundance and prevalence of gene clusters and genomes are recovered through shotgun metagenomes. [ 32 ]  The combination of metagenomes with pangenomes, also referred to as "metapangenomics", reveals the population-level results of habitat-specific filtering of the pangenomic gene pool. [ 33 ]


Other authors consider that Metapangenomics expands the concept of pangenome by incorporating  gene  sequences obtained from uncultivated  microorganisms  by a  metagenomics  approach. A metapangenome comprises both sequences from metagenome-assembled genomes ( MAGs ) and from  genomes  obtained from cultivated microorganisms. [ 34 ]  Metapangenomics has been applied to assess diversity of a community, microbial niche adaptation, microbial evolution, functional activities, and interaction networks of the community. [ 35 ]  The Anvi'o platform developed a workflow that integrates analysis and visualization of metapangenomes by generating pangenomes and study them in conjunction with metagenomes. [ 32 ]


In 2018, 87% of the available whole genome sequences were bacteria fueling researchers interest in calculating prokaryote pangenomes at different taxonomic levels. [ 22 ]   In 2015, the pangenome of 44 strains of  Streptococcus pneumoniae  bacteria shows few new genes discovered with each new genome sequenced (see figure).  In fact, the predicted number of new genes dropped to zero when the number of genomes exceeds 50 (note, however, that this is not a pattern found in all species). This would mean that  S. pneumoniae  has a 'closed pangenome'. [ 37 ]  The main source of new genes in  S. pneumoniae  was  Streptococcus mitis  from which  genes were transferred horizontally . The pan-genome size of  S. pneumoniae  increased logarithmically with the number of strains and linearly with the number of polymorphic sites of the sampled genomes, suggesting that acquired genes accumulate proportionately to the age of clones. [ 36 ]  Another example of prokaryote pan-genome is  Prochlorococcus , the core genome set is much smaller than the pangenome, which is used by different ecotypes of  Prochlorococcus . [ 38 ]  Open pan-genome has been observed in environmental isolates such as  Alcaligenes  sp. [ 39 ]  and  Serratia  sp., [ 40 ]  showing a sympatric lifestyle. Nevertheless, open pangenome is not exclusive to free living microorganisms, a 2015 study on  Prevotella  bacteria isolated from  humans , compared the gene repertoires of its species derived from different body sites of human. It also reported an open pan-genome showing vast diversity of gene pool. [ 41 ]


Archaea also have some pangenome studies.  Halobacteria  pangenome shows the following gene families in the pangenome subsets: core (300), variable components (Softcore: 998, Cloud:36531, Shell:11784). [ 42 ]


Eukaryote  organisms such as  fungi ,  animals  and  plants  have also shown evidence of pangenomes. In four fungi species whose pangenome has been studied, between 80 and 90% of gene models were found as core genes. The remaining accessory genes were mainly involved in pathogenesis and antimicrobial resistance. [ 43 ]


In animals, the human pangenome is being studied. In 2010 a study estimated that a complete human pan-genome would contain ~19–40 Megabases of novel sequence not present in the extant reference  human genome . [ 44 ]  The  Human Pangenome consortium  has the goal to acknowledge the human genome diversity. In 2023, a draft human pangenome reference was published. [ 45 ]  It is based on 47 diploid genomes from persons of varied ethnicity. [ 45 ]  Plans are underway for an improved reference capturing still more biodiversity from a still wider sample. [ 45 ]


Among plants, there are examples of pangenome studies in model species, both diploid  [ 9 ]  and polyploid, [ 10 ]  and a growing list of crops. [ 46 ] [ 47 ]
Pangenomes have shown promise as a tool in plant breeding by accounting for  structural variants  and  SNPs  in non-reference genomes, which helps to solve the problem of  missing heritability  that persists in  genome wide association studies . [ 48 ]  An emerging plant-based concept is that of pan-NLRome, which is the repertoire of nucleotide-binding leucine-rich repeat (NLR) proteins, intracellular immune receptors that recognize pathogen proteins and confer disease resistance. [ 49 ]


Virus  does not necessarily have genes extensively shared by clades such as is the case of  16S  in  bacteria , and therefore the core genome of the full Virus Domain is empty. Nevertheless, several studies have calculated the pangenome of some viral lineages. The core genome from six species of pandoraviruses comprises 352 gene families only 4.7% of the pangenome, resulting in an open pangenome. [ 50 ]


The number of sequenced genomes is continuously growing "simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets". [ 51 ]  Pangenome graphs are emerging data structures designed to represent pangenomes and to efficiently map reads to them. They have been reviewed by Eizenga et al.  [ 52 ]


As interest in pangenomes increased, there have been several  software  tools developed to help analyze this kind of data.
To start a pangenomic analysis the first step is the homogenization of genome annotation. [ 23 ]  The same software should be used to annotate all genomes used, such as GeneMark [ 53 ]  or RAST. [ 54 ]   In 2015, a group reviewed the different kinds of analyses and tools a researcher may have available. [ 55 ]  There are seven kinds of software developed to analyze pangenomes: Those dedicated to cluster homologous genes; identify  SNPs ; plot pangenomic profiles; build phylogenetic relationships of orthologous genes/families of strains/isolates; function-based searching; annotation and/or curation; and visualization. [ 55 ]


The two most cited software tools for pangenomic analysis at the end of 2014 [ 55 ]  were Panseq [ 56 ]  and the pan-genomes analysis pipeline (PGAP). [ 57 ]  Other options include BPGA – A Pan-Genome Analysis Pipeline for prokaryotic genomes, [ 58 ]  GET_HOMOLOGUES, [ 59 ]  Roary. [ 60 ]  and PanDelos. [ 61 ]  In 2015 a review focused on prokaryote pangenomes [ 62 ]  and another for plant pan-genomes were published. [ 63 ]  Among the first software packages designed for plant pangenomes were PanTools. [ 64 ]  and GET_HOMOLOGUES-EST. [ 11 ] [ 59 ]  In 2018 panX was released, an interactive web tool that allows inspection of gene families evolutionary history. [ 65 ]  panX can display an alignment of genomes, a phylogenetic tree, mapping of mutations and inference about gain and loss of the family on the core-genome phylogeny. In 2019 OrthoVenn 2.0  [ 66 ]  allowed comparative visualization of families of homologous genes in Venn diagrams up to 12 genomes. In 2023,  BRIDGEcereal was developed to survey and graph indel-based haplotypes from pan-genome through a gene model ID. [ 67 ]