Understanding and analyzing the vast data that comes from population genomics studies requires various mathematical models. One method of analyzing this vast data is through  QTL  mapping. QTL mapping has been used to help find the genes that are responsible for adaptive phenotypes. [ 15 ]  To quantify the genetic diversity within a population a value known as the  fixation index , or F ST  is used. When used with  Tajima's D , F ST  has been used to show how selection acts upon a population. [ 16 ]  The  McDonald-Kreitman test  (or MK test) is also favored when looking for selection because it is not as sensitive to changes in a species' demography that would throw off other selection tests. [ 17 ]


Most developments within population genomics have to do with increases in the sequencing technology. For example, restriction-site associated DNA sequencing, or RADSeq is a relatively new technology that sequences at a lower complexity and delivers higher resolution at a reasonable cost. [ 18 ]  High-throughput sequencing technologies are also a rapidly growing field that allows for more information to be gathered on genomic divergence during speciation. [ 19 ]  High-throughput sequencing is also very useful for SNP detection, which plays a key role in personalized medicine. [ 20 ]  Another relatively new approach is reduced-representation library (RRL) sequencing which discovers and genotypes SNPs and also doesn't require reference genomes. [ 21 ]


In  genomics , the  postgenomic era  (or  post-genomic era ) refers to the time period from after the completion of the  Human Genome Project  to the present day. The name refers to the fact that the  genetic epistemology  of contemporary science has progressed beyond the  gene -centered view of the earlier genomic era. [ 1 ]  It is defined by the widespread availability of both the human genome sequence and of the complete genomes of many reference organisms. [ 2 ]


The postgenomic era is characterized by a  paradigm shift  in which new genetic research has upended many dogmas about the way in which genes influence  phenotypes , and the way in which the term "gene" itself is defined. [ 3 ] [ 4 ]  This has included a new conceptualization of genes as being constituted during " genome  expression", [ 5 ]  and the creation of the discipline of  functional genomics  to analyze genomic data and convert it to useful information. [ 6 ]  It has also seen major changes in the way scientific research is conducted and its results publicized, with  open science  initiatives allowing knowledge creation to occur well outside the traditional environment of the laboratory. [ 7 ]  This has led to extensive debate about whether the best way to conduct genomic research is at a small or large scale. [ 8 ]


Soon after the HGP's results were initially announced in 2000, researchers predicted that these results would lead to individualized treatment and more accurate testing for human diseases. [ 9 ]  More recently, researchers have suggested that the way in which human diseases are classified needs to be updated in light of the results of the HGP. [ 10 ]


Predictive genomics  is at the intersection of multiple disciplines:  predictive medicine ,  personal genomics  and  translational bioinformatics . Specifically, predictive genomics deals with the future  phenotypic  outcomes via  prediction  in areas such as complex multifactorial  diseases  in humans. [ 1 ]  To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in  genome-wide association  (GWA) studies. [ 2 ]  The identification of associated  single-nucleotide polymorphisms  (variation of a DNA sequence in a population) underpin GWA studies in complex diseases that have ranged from  Type 2 Diabetes  (T2D),  Age-related macular degeneration  (AMD) and  Crohn's disease .


Although the  Human Genome Project  has progressively improved the fidelity of sequence determination, the overbearing complexity of the genome hinders the identification of associated or ultimately causal variants. [ 3 ]  In particular, there are likely to be a large number of implicated  genetic loci  which exhibit small marginal effects. [ 4 ]


A number of short- and long-term goals exist for predictive genomics. The identification of associated variants underpin all other downstream endeavors that point toward better data-cum-knowledge outcomes. In particular, those outcomes that facilitate clinical improvement and individualised healthcare further lead to actionable measures in  diagnosis ,  prognosis  and  prevention .


Whilst the single-gene, single-disease hypothesis holds for Mendelian disorders such as  Huntington's disease  and  cystic fibrosis , complex diseases and traits are affected by a number of gene loci and genetic variants with varying risk. [ 1 ]  A precursor to the development of preventative, prognostic and diagnostic tools in these diseases requires mapping genetic loci in disease etiology and discovering causal mutations. [ 5 ]  Creating a ‘genomic profile’ of individuals with the number of variants at the genome-wide level facilitates not only the prediction of disease prior to onset, but also serves as a primer to increasing the knowledge of causal variants. [ 6 ]


The foremost difficulty in achieving this goal is to understand the functionality of these variants with respect to areas of physiological and molecular importance in conjunction with phenotype. [ 4 ]  If associated variants are mapped to sequences with unknown function, then this restricts the ability for specific targeting in areas of interest. Therefore, the ability for predictive genomics to succeed also depends upon other related areas such as the functional annotation of the genome ( ENCODE ). [ 7 ]


The identification of causal variants, genes and pathways leads to opportunities that bridge the divide between research and clinical usage. If successful, the subsequent discovery of therapeutic targets within implicated biological pathways have consequences for both treatment and prevention. [ 8 ]  Furthermore, the downstream effect of identifying disease-relevant biomarkers allow for improvements to monitoring disease progression and response-to-treatment, where the implementation of these results into  clinical decision support systems  (CDSS) facilitate personalised medicine and outcomes. [ 9 ]  Even if only marginally effectual, the repeated replication of associated variants can offer significant translational value. [ 7 ]


The significance of translation from research to clinical usage relates to use of the complete knowledge of an individual to develop personalised approaches to disease management. The caveat with this is that there have been difficulties in both prediction and inference for complex diseases. [ 10 ]  Therefore, unless individuals have an overwhelming high or low number of risk  alleles , there is a limit to the predictive accuracy of their ‘genomic profiles’. However, preliminary examples of predictive genomics for personalising healthcare include: using an individual's  gene expression  data to monitor progress to treatment, [ 11 ]  or using the genomic profile of the P450 drug metabolising system of individuals to assist dosage and selection. [ 12 ]


In the table below is a performance comparison of diseases selected on disease frequency and known heritability estimates, with use of  single-nucleotide polymorphism  (SNP) based models reflecting known genetic factors for a European population (subject to change as more associations are discovered). [ 13 ]

 K

 {\displaystyle K}

  denotes lifetime morbid risk,

 h

 L

 2

 {\displaystyle h_{L}^{2}}

  denotes heritability of liability,

 A
 U
 C

 {\displaystyle AUC}

  denotes area under the ROC curve.


In the applications of predictive genomics below, these complex diseases either lack or are lacking reliable diagnostics for disease. Given the medical consequences of these diseases, the economic impact is also significant. However, none of the use cases below has been translated into the clinic.


Age-related macular degeneration  (AMD) is one of the flagship complex diseases from the genomic revolution with over 19 associated genetic loci replicated in GWA studies. [ 14 ]  In particular, the first significant genetic risk variant was identified in the complement factor H( CFH ) gene in 2005 motivating the search for more genetic variants in the disease. Over the past decade, a number of models have been proposed to assess individual risk to AMD. [ 15 ]  The genetic predisposition of AMD risk varies from 45% to 71% where highly effectual  odds ratios  (OR) have been reported (greater than 2.0 per allele in some cases). [ 14 ]  In 2013, a comprehensive case-control GWA study with approximately 77,000 observations involving 18 international research groups from the International AMD Genetics Consortium implicated 19 gene loci and 9  biological pathways  including the regulation of complement,  lipid metabolism  and  angiogenic  activity. The predictive performance of the full model including all 19 loci exhibited 0.74  AUC  - according to Jakobsdottir et al., 0.75 AUC is sufficient to distinguish between extreme cases and controls. [ 16 ]  In particular, of the 19 associated gene loci, there were 7 newly discovered loci, which the authors point to as additional entry points into AMD etiology and  drug targets . [ 14 ]


Type 2 diabetes  (T2D), an extremely common metabolic disorder, has demonstrated interplay between many environmental and genetic risk factors leading to disease onset. [ 17 ]  A number of risk assessment models incorporating a number of demographic, environmental and clinical risk factors are already shown to elicit reasonable discrimination in case-control studies; it has been proposed that identifying genetic variants that contribute to T2D as for standalone prediction or in conjunction with current risk models can improve prediction of T2D risk, if current models lack sufficient coverage of the full effect of an individual's genotype. [ 18 ]  Approximately 20 associated SNPs have been replicated in T2D; however, their effect sizes do not seem to be substantial: OR 1.37 for SNPs in the  TCF7L2  gene purported to give highest genetic risk. [ 19 ]


In 2009, a study was conducted on the WTCCC (GWA study involving 7 cohorts with 7 diseases: including  bipolar disorder ,  Crohn's disease ,  hypertension ,  rheumatoid arthritis ,  Type I Diabetes  (T1D) and  Type II Diabetes  (T2D)). [ 20 ]  With particular attention to T2D, Evans et al. were able to discern a marginal increase in AUC (+0.04) based on genome-wide information with respect to known susceptible variants. [ 20 ]  However, non-genetic based tests such as the Cambridge and Framingham offspring risk scores have been purported to perform better than genetic-risk models with 20 loci. [ 18 ]  Moreover, the addition of genetic risk with these phenotypical models did not produce statistically significant AUC results.


Celiac disease  (CD) is a complex immune disorder that has been found to have strong genetic links in disease. In particular,  human leukocyte antigen   (HLA) genes are strongly implicated in CD development and HLA testing is undertaken in clinical practice. However, although there are serological and histological tests available for CD, these clinical screenings have been found to generate false positives. [ 21 ]  In 2014, Abraham et al. used a genomic risk score (GRS) generated over 6 cohorts with an AUC of 0.86 to 0.90. [ 21 ]


For predictive genomics to address their objectives, there must be an improvement in the accuracy of prediction through added methods or improvements to current techniques and to demonstrate that there is bonafide improvement in patient outcomes. Currently, although  AUC  (Area Under the ROC) is the de facto metric in comparing and evaluating the performance of predictive models, there is no consensus as to what kind of score is sufficient for clinical use. Jakobsdottir et al. states that 0.75 AUC is sufficient for discriminating between clear cases and controls; however, this is still arbitrary. [ 16 ]  The  positive predictive value  (PPV) must be high enough to avoid a higher prevalence of false-positives.


SNPs identified in GWA studies are considered to give better predictive performance if they have high effect sizes of Odds Ratios (OR). A case study involving 5 use cases of genomic prediction demonstrate that SNPs with extremely small p-values, and by implication extreme OR do not give extreme differences in discrimination. [ 16 ]  They point out that use of significantly associated genetic variants does not necessarily lead to better classification. Alternatively,  CNV  (copy number variants) have been proposed to usurp SNPs as better candidates for prediction with  BMI  stratified across different ethnicities demonstrating better, although marginal, improvement of CNVs over SNPs for prediction. [ 22 ]  Furthermore, a comparison of over 10 complex disorders in prediction with respect to family history and SNPs for prediction did not suggest better discrimination with SNPs. [ 13 ]


Currently, the prevailing standard of risk models focus on univariate analysis rather than focusing upon interactions of higher order. Therefore, although typical GWA studies are able to detect a number of statistically significant loci, they have not been sufficient to fully explain the estimates of theoretical genetic heritability. [ 10 ]  It has been demonstrated by Goudey et al. that both 2-way and 3-way interactions between SNPs are able to explain trait variance relative to single SNPs. [ 23 ]  Goudey et al. also states that the barrier to expansion of higher order interactions has been limited by the intractability of exhaustive search techniques (see  NP-complete ).


The issues surrounding sample size and number of variants become exacerbated particularly when GWA studies consider variants of volume in the order of millions. Therefore, due to the current constraints in the curse of dimensionality, prior screening methods that decrease the number of loci to below the number of observations may be used before modelling disease risk. [ 24 ]  Hayes et al. states that population size must be >100,000 in order to achieve high accuracy under their model assumptions; the exception is the case where there is a small effective population size. [ 25 ]  Furthermore, ethnic specific GWA studies show that each group has varied detectability of variants in terms of: frequency,  linkage disequilibrium  – the co-inheritance of SNPs through generations – and the actual loci themselves. [ 26 ]


Predictive genomics has not been constrained to prediction of complex diseases. For instance, Hayes et al. uses genomic prediction for livestock, crop and forage species selection, where predicted results are currently in use. [ 25 ]  Furthermore, Kambouris et al. discusses the use of ‘genomic profiles’ for the performance of elite athletes noting individualised and personalised training regimens for both dietary and physical aspects. [ 27 ]  Additionally, Kayser et al. point to  DNA profiling  in forensics as a beneficiary of the genomic revolution. [ 28 ]  Functionally validating novel genetic findings is crucial in rare disease. However, the analysis of individual genetic variants often requires several years of work. Variants that are most likely to occur and present as disease-causing can be predicted; distinct from and supplementary to pathogenicity prediction. This application guides research to test the effect of top candidate variants in preparation for novel disease cases. [ 29 ]


The  Protein Information Resource  ( PIR ), located at  Georgetown University Medical Center , is an integrated public  bioinformatics  resource to support  genomic  and  proteomic  research, and scientific studies. It contains protein sequences databases  [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ]


PIR was established in 1984 by the National Biomedical Research Foundation as a resource to assist researchers and customers in the identification and interpretation of  protein sequence  information. Prior to that, the foundation compiled the first comprehensive collection of macromolecular sequences in the Atlas of Protein Sequence and Structure, published from 1964 to 1974 under the editorship of  Margaret Dayhoff . Dayhoff and her research group pioneered in the development of computer methods for the comparison of protein sequences, for the detection of distantly related sequences and duplications within sequences, and for the inference of evolutionary histories from alignments of protein sequences. [ 8 ]


Winona Barker and  Robert Ledley  assumed leadership of the project after the death of Dayhoff in 1983. In 1999,  Cathy H. Wu  joined the National Biomedical Research Foundation, and later on Georgetown University Medical Center, to head the bioinformatics efforts of PIR, and has served first as Principal Investigator and, since 2001, as Director. [ citation needed ]


For four decades, PIR has provided many protein databases and analysis tools freely accessible to the scientific community, including the Protein Sequence Database, the first international database (see PIR-International), which grew out of Atlas of Protein Sequences and Structure. [ citation needed ]


In 2002, PIR – along with its international partners, the  European Bioinformatics Institute  and the  Swiss Institute of Bioinformatics  – were awarded a grant from NIH to create  UniProt , a single worldwide database of protein sequence and function, by unifying the Protein Information Resource-Protein Sequence Database,  Swiss-Prot , and  TrEMBL  databases. As of 2010 [update] , PIR offers a wide variety of resources mainly oriented to assist the propagation and standardization of protein annotation: PIRSF, [ 9 ]  iProClass, and iProLINK.


The Protein Ontology is another popular database released by the Protein Information Resource. [ 10 ] [ 11 ]


Proteogenomics  is a field of biological research that utilizes a combination of  proteomics ,  genomics , and  transcriptomics  to aid in the discovery and identification of  peptides.  Proteogenomics is used to identify new peptides by comparing  MS/MS  spectra against a protein database that has been derived from genomic and transcriptomic information. Proteogenomics often refers to studies that use proteomic information, often derived from  mass spectrometry , to improve  gene  annotations. The utilization of both proteomics and genomics data alongside advances in the availability and power of spectrographic and chromatographic technology led to the emergence of proteogenomics as its own field in 2004.


Proteomics  deals with proteins in the same way that  Genomics  studies the genetic code of entire organisms, while  Transcriptomics  deals with the study of  RNA  sequencing and transcripts. While all three fields might use forms of  mass spectrometry  and  chromatography  to identify and study the functions of  DNA ,  RNA , and proteins, proteomics relies on the assumption that current gene models are correct and that all relevant protein sequences can be found in a reference database such as the  Proteomics Identifications Database . Proteogenomics helps eliminate this reliance on existing, limited genetic models by combining datasets from multiple fields in order to produce a database of proteins or genetic markers. In addition, the emergence of novel protein sequences due to  mutations  often cannot be accounted for in traditional proteomic databases, but can be predicted and studied using a synthesis of genomic and transcriptomic data.


The resulting research has applications in improving  gene annotations , studying mutations, and understanding the effects of  genetic manipulation .


More recently, the joint profiling of surface proteins and mRNA transcripts from single cells by methods such as  CITE-Seq  and ESCAPE  [ 1 ]  has been referred to as single-cell proteogenomics, [ 2 ] [ 3 ] [ 4 ]  although the goals of these studies are not related to peptide identification.  Since 2019 these methods are more commonly referred to as multimodal omics or multi-omics. [ 5 ]


Proteogenomics emerged as an independent field in 2004, based on the integration of technological advancements in next-generation sequencing genomics, and mass spectrometry proteomics. [ 6 ]  The term itself came into use that year, with the publication of a paper by  George Church ’s research group describing their discovery of a proteogenomic mapping technique that utilized proteomics data to better annotate the genome of the bacteria  M. pneumoniae .  By using a modern protein database, the lab mapped peptides detected in a whole cell onto a  genetic scaffold  using tandem mass spectrometry, then used the generated "hits" in order to create a "proteogenomic map" based on traditional genetic signals. The resulting map proved extremely accurate, with over 81% of predicted  genomic reading frames  being detected in the bacterial cells studied. In addition, the lab discovered several new frames not predicted via purely genetic methods, as well as some evidence supporting the idea that several predictions based genetic models could be false, proving the accuracy and cost-effectiveness of the hybrid technique. [ 7 ] [ 8 ]


The field expanded over the next two decades, initially using proteomics data to aid in refining genetic models via protein databases. [ 6 ]  In 2020s, one of the most common technique for identifying peptides involves using  tandem mass spectrometry . This technique originated with Eng and Yates in 1994 which involves comparing a theoretical peptide fragment spectrum to compare an experimentally derived peptide spectrum to and outputting the most likely matches found. [ 7 ]  However, in the absence of an established peptide database, Proteogenomics instead compares the experimental spectrum to a genomic database instead which can then be used for genome annotation - as described in George Church's work. [3]  The latter technique has become more widely used over the last decade in large part due to the increasing affordability and speed of genomic sequencing techniques coupled with the increasing sensitivity of mass spectrometry-based proteomics. [ 6 ]


The main idea behind the proteogenomic approach is to identify peptides by comparing MS/MS data to protein databases that contain predicted protein sequences. [ 9 ]  The protein database is generated in a variety of ways through the utilization of genomic and transcriptomic data. Below are some of the ways in which protein databases are generated:


Six-frame translations  can be utilized to generate a database that predicts protein sequences. The limitation of this method is that databases will be very large due to the number of sequences that are generated, some of which do not exist in nature. [ 10 ]


In this method, a protein base is generated by gene predicting algorithms that enable the identification of protein  coding regions . The database is similar to one generated through six-frame translation in regards to the fact that the databases can be very large. [ 10 ]


Six-frame translations can utilize an  expressed sequence tag  (EST) to generate protein databases. EST data provide transcription information that can aid in the creation of the database. The database can be very large and has the disadvantage of having multiple copies of a given sequence present; however, this problem can be circumvented by compressing the protein sequence generated through computational strategies. [ 10 ]


Protein databases can also be created by using  RNA  sequencing data, annotated RNA transcripts, and variant protein sequences. Also, there are other more specialized protein databases that can be made to appropriately identify the peptide of interest. [ 10 ]


Another method in the identification of proteins through proteogenomics is comparative proteogenomics. Comparative proteogenomics compares proteomic data from multiple related species concurrently and exploits the homology between their proteins to improve annotations with higher statistical confidence. [ 11 ] [ 12 ]


Proteogenomics can be applied in different ways. One application is the improvement of gene annotations in various organisms. Gene annotation involves discovering genes and their functions. [ 13 ]
Proteogenomics has become especially useful in the discovery and improvement of gene annotations in prokaryotic organisms. For example, various microorganisms have had their genomic annotation studied through the proteogenomic approach including,  Escherichia coli ,  Mycobacterium , and  multiple species of  Shewanella  bacteria.
 [ 14 ]


Besides improving gene annotations, proteogenomic studies can also provide valuable information about the presence of programmed  frameshifts , N-terminal  methionine  excision,  signal peptides ,   proteolysis  and other  post-translational modifications . [ 15 ] [ 11 ]  Proteogenomics has potential applications in medicine, especially to  oncology  research. Cancer occurs through genetic  mutations  such as  methylation ,  translocation , and  somatic  mutations. Research has shown that both genomic and proteomic information are needed to understand the molecular variations that lead to cancer. [ 16 ] [ 17 ]  Proteogenomics has aided in this through the  identification of protein sequences that may have functional roles in cancer. [ 18 ]  A specific example of this occurred in a study involving colon cancer that resulted in the discovery of potential targets for cancer treatment. [ 16 ]  Proteogenomics has also led to personalized cancer targeting immunotherapies, where antibody epitopes for cancer antigens are predicted using proteogenomics to create medicines that act on the patient's specific tumor. [ 19 ]  In addition to treatment, proteogenonomics may provide insight into cancer diagnosis. In studies involving colon and rectal cancer, proteogenomics was utilized to identify somatic mutations. The identification of somatic mutations in patients could be used to diagnose cancer in patients. In addition to direct applications in cancer treatment and diagnosis, a proteogenomic approach can be used to study proteins that result in resistance to  chemotherapy . [ 17 ]


Proteogenomics may offer methods of peptide identification without having the disadvantage of incomplete or inaccurate protein databases faced by proteomics; however, there are incurring challenges with the proteogenomic approach. [ 10 ]  One of the biggest challenges of proteogenomics is the sheer size of protein databases generated. statistically, a large protein database is more likely to result in the incorrect matching of the data from the protein database to the MS/MS data, this issue can hinder the identification of new peptides. False positives are also an issue through proteogenomic approaches. false positives can occur as a result of extremely large protein data bases where miss-matched data leads to incorrect identification. Another issue is the incorrect matching of MS/MS spectra to protein sequence data that corresponds to a similar peptide instead of the actual peptide. There are cases of receiving data of a peptide located at multiple gene sites, this can lead to data that can be interpreted in different ways. Despite these challenges, there are ways to reduce many of the errors that occur. For example, when dealing with a very large protein database, one could compare the identified novel peptide sequences to all of the sequences within the database and then compare the post translational modifications. Next it can be determined if the two sequences represent the same peptide or if they are two different peptides. [ 10 ]


Proteomics  is the large-scale study of  proteins . [ 1 ] [ 2 ]  Proteins are vital  macromolecules  of all living organisms, with many functions such as the formation of structural fibers of  muscle tissue , enzymatic digestion of food, or synthesis and replication of  DNA . In addition, other kinds of proteins include  antibodies  that protect an organism from infection, and  hormones  that send important signals throughout the body.


The  proteome  is the entire set of proteins produced or modified by an organism or system. Proteomics enables the identification of ever-increasing numbers of proteins. This varies with time and distinct requirements, or stresses, that a cell or organism undergoes. [ 3 ]


Proteomics is an interdisciplinary domain that has benefited greatly from the genetic information of various genome projects, including the  Human Genome Project . [ 4 ]  It covers the exploration of proteomes from the overall level of protein composition, structure, and activity, and is an important component of  functional genomics .


Proteomics  generally denotes the large-scale experimental analysis of proteins and proteomes, but often refers specifically to  protein purification  and  mass spectrometry . Indeed, mass spectrometry is the most powerful method for analysis of proteomes, both in large samples composed of millions of cells [ 5 ]  and in single cells. [ 6 ] [ 7 ]


The first studies of proteins that could be regarded as proteomics began in 1974, after the introduction of the two-dimensional gel and mapping of the proteins from the bacterium  Escherichia coli . [ 8 ]


Proteome  is a blend of the words "protein" and "genome". It was coined in 1994 by then-Ph.D student  Marc Wilkins  at  Macquarie University , [ 9 ]  which founded the first dedicated proteomics laboratory in 1995. [ 10 ] [ 11 ]


After  genomics  and  transcriptomics , proteomics is the next step in the study of biological systems. It is more complicated than genomics because an organism's genome is more or less constant, whereas proteomes differ from cell to cell and from time to time. Distinct genes are  expressed  in different cell types, which means that even the basic set of proteins produced in a cell must be identified. [ 12 ]


In the past this phenomenon was assessed by RNA analysis, which was found to lack correlation with protein content. [ 13 ] [ 14 ]  It is now known that  mRNA  is not always translated into protein, [ 15 ]  and the amount of protein produced for a given amount of mRNA depends on the gene it is transcribed from and on the cell's physiological state. Proteomics confirms the presence of the protein and provides a direct measure of its quantity. [ citation needed ]


Not only does the translation from mRNA cause differences, but many proteins also are subjected to a wide variety of chemical modifications after translation. The most common and widely studied post-translational modifications include phosphorylation and glycosylation. Many of these post-translational modifications are critical to the protein's function. [ citation needed ]


One such modification is  phosphorylation , which happens to many  enzymes  and structural proteins in the process of  cell signaling . The addition of a phosphate to particular amino acids—most commonly  serine  and  threonine [ 16 ]  mediated by serine-threonine  kinases , or more rarely  tyrosine  mediated by tyrosine  kinases —causes a protein to become a target for binding or interacting with a distinct set of other proteins that recognize the phosphorylated domain. [ citation needed ]


Because protein phosphorylation is one of the most studied protein modifications, many "proteomic" efforts are geared to determining the set of phosphorylated proteins in a particular cell or tissue-type under particular circumstances. This alerts the scientist to the signaling pathways that may be active in that instance.


Ubiquitin  is a small protein that may be affixed to certain protein substrates by enzymes called  E3 ubiquitin ligases . Determining which proteins are poly-ubiquitinated helps understand how protein pathways are regulated. This is, therefore, an additional legitimate "proteomic" study. Similarly, once a researcher determines which substrates are ubiquitinated by each ligase, determining the set of ligases expressed in a particular cell type is helpful. [ citation needed ]


In addition to  phosphorylation  and  ubiquitination , proteins may be subjected to (among others)  methylation ,  acetylation ,  glycosylation ,  oxidation , and  nitrosylation . Some proteins undergo all these modifications, often in time-dependent combinations. This illustrates the potential complexity of studying protein structure and function.


A cell may make different sets of proteins at different times or under different conditions, for example during  development ,  cellular differentiation ,  cell cycle , or  carcinogenesis . Further increasing proteome complexity, as mentioned, most proteins are able to undergo a wide range of post-translational modifications.


Therefore, a "proteomics" study may become complex very quickly, even if the topic of study is restricted. In more ambitious settings, such as when a  biomarker  for a specific cancer subtype is sought, the proteomics scientist might elect to study multiple blood serum samples from multiple cancer patients to minimise confounding factors and account for experimental noise. [ 17 ]  Thus, complicated experimental designs are sometimes necessary to account for the dynamic complexity of the proteome.


Proteomics gives a different level of understanding than genomics for many reasons:


Reproducibility . One major factor affecting reproducibility in proteomics experiments is the simultaneous  elution  of many more peptides than mass spectrometers can measure. This causes  stochastic  differences between experiments due to  data-dependent acquisition  of tryptic peptides. Although early large-scale shotgun proteomics analyses showed considerable variability between laboratories, [ 20 ] [ 21 ]  presumably due in part to technical and experimental differences between laboratories, reproducibility has been improved in more recent mass spectrometry analysis, particularly on the protein level. [ 22 ]  Notably,  targeted proteomics  shows increased reproducibility and repeatability compared with shotgun methods, although at the expense of data density and effectiveness. [ 23 ]


Data quality . Proteomic analysis is highly amenable to automation and large data sets are created, which are processed by software algorithms. Filter parameters are used to reduce the number of false hits, but they cannot be completely eliminated. Scientists have expressed the need for awareness that proteomics experiments should adhere to the criteria of analytical chemistry (sufficient data quality, sanity check, validation). [ 24 ] [ 25 ] [ 26 ] [ 27 ]


In proteomics, there are multiple methods to study proteins. Generally, proteins may be detected by using either  antibodies  (immunoassays), electrophoretic separation or  mass spectrometry . If a complex biological sample is analyzed, either a very specific antibody needs to be used in quantitative dot blot analysis (QDB), or biochemical separation then needs to be used before the detection step, as there are too many analytes in the sample to perform accurate detection and quantification.


Antibodies  to particular proteins, or their modified forms, have been used in  biochemistry  and  cell biology  studies. These are among the most common tools used by molecular biologists today. There are several specific techniques and protocols that use antibodies for protein detection. The  enzyme-linked immunosorbent assay  (ELISA) has been used for decades to detect and quantitatively measure proteins in samples.  The  western blot  may be used for detection and quantification of individual proteins, where in an initial step, a complex protein mixture is separated using  SDS-PAGE  and then the protein of interest is identified using an antibody. [ citation needed ]


Modified proteins may be studied by developing an  antibody  specific to that modification. For example, some antibodies only recognize certain proteins when they are tyrosine- phosphorylated , they are known as phospho-specific antibodies. Also, there are antibodies specific to other modifications. These may be used to determine the set of proteins that have undergone the modification of interest. [ citation needed ]


Immunoassays can also be carried out using recombinantly generated immunoglobulin derivatives or synthetically designed protein scaffolds that are selected for high antigen specificity. Such binders include single domain antibody fragments (Nanobodies), [ 28 ]  designed ankyrin repeat proteins (DARPins) [ 29 ]  and aptamers. [ 30 ]


Disease detection at the molecular level is driving the emerging revolution of early diagnosis and treatment. A challenge facing the field is that protein biomarkers for early diagnosis may be present in very low abundance. The lower limit of detection with conventional immunoassay technology is the upper femtomolar range (10 −13  M). Digital immunoassay technology has improved detection sensitivity three logs, to the attomolar range (10 −16  M). This capability has the potential to open new advances in diagnostics and therapeutics, but such technologies have been relegated to manual procedures that are not well suited for efficient routine use. [ 31 ]


While protein detection with antibodies is still very common in molecular biology, other methods have been developed as well, that do not rely on an antibody. These methods offer various advantages, for instance they often are able to determine the sequence of a protein or peptide, they may have higher throughput than antibody-based, and they sometimes can identify and quantify proteins for which no antibody exists.


One of the earliest methods for protein analysis has been  Edman degradation  (introduced in 1967) where a single  peptide  is subjected to multiple steps of chemical degradation to resolve its sequence. These early methods have mostly been supplanted by technologies that offer higher throughput. [ citation needed ]


More recently implemented methods use  mass spectrometry -based techniques, a development that was made possible by the discovery of "soft ionization" methods developed in the 1980s, such as  matrix-assisted laser desorption/ionization (MALDI)  and  electrospray ionization (ESI) . These methods gave rise to the  top-down  and the  bottom-up proteomics  workflows where often additional separation is performed before analysis (see below).


For the analysis of complex biological samples, a reduction of sample complexity is required. This may be performed off-line by  one-dimensional  or  two-dimensional  separation. More recently, on-line methods have been developed where individual peptides (in bottom-up proteomics approaches) are separated using  reversed-phase chromatography  and then, directly ionized using  ESI ; the direct coupling of separation and analysis explains the term "on-line" analysis.


Several hybrid technologies use antibody-based purification of individual analytes and then perform mass spectrometric analysis for identification and quantification. Examples of these methods are the
 MSIA (mass spectrometric immunoassay) , developed by Randall Nelson in 1995, [ 32 ]  and the SISCAPA (Stable Isotope Standard Capture with Anti-Peptide Antibodies) method, introduced by Leigh Anderson in 2004. [ 33 ]


Fluorescence two-dimensional differential gel electrophoresis (2-D DIGE) [ 34 ]  may be used to quantify variation in the 2-D DIGE process and establish statistically valid thresholds for assigning quantitative changes between samples. [ 34 ]


Comparative proteomic analysis may reveal the role of proteins in complex biological systems, including reproduction. For example, treatment with the insecticide triazophos causes an increase in the content of brown planthopper ( Nilaparvata lugens  (Stål)) male accessory gland proteins (Acps) that may be transferred to females via mating, causing an increase in fecundity (i.e. birth rate) of females. [ 35 ]  To identify changes in the types of accessory gland proteins (Acps) and reproductive proteins that mated female planthoppers received from male planthoppers, researchers conducted a comparative proteomic analysis of mated  N. lugens  females. [ 36 ]  The results indicated that these proteins participate in the reproductive process of  N. lugens  adult females and males. [ 36 ]


Proteome analysis of  Arabidopsis peroxisomes [ 37 ]  has been established as the major unbiased approach for identifying new peroxisomal proteins on a large scale. [ 37 ]


There are many approaches to characterizing the human proteome, which is estimated to contain between 20,000 and 25,000 non-redundant proteins. The number of unique protein species likely will increase by between 50,000 and 500,000 due to RNA splicing and proteolysis events, and when post-translational modification also are considered, the total number of unique human proteins is estimated to range in the low millions. [ 38 ] [ 39 ]


In addition, the first promising attempts to decipher the proteome of animal tumors have recently been reported. [ 40 ]
This method was used as a functional method in  Macrobrachium rosenbergii  protein profiling. [ 41 ]


Proteomics has steadily gained momentum over the past decade with the evolution of several approaches. Few of these are new, and others build on traditional methods. Mass spectrometry-based methods, affinity proteomics, and micro arrays are the most common technologies for large-scale study of proteins.


There are two mass spectrometry-based methods currently used for protein profiling. The more established and widespread method uses high resolution, two-dimensional electrophoresis to separate proteins from different samples in parallel, followed by selection and staining of differentially expressed proteins to be identified by mass spectrometry. Despite the advances in 2-DE and its maturity, it has its limits as well.
The central concern is the inability to resolve all the proteins within a sample, given their dramatic range in expression level and differing properties. The combination of pore size, and protein charge, size and shape can greatly determine migration rate which leads to other complications. [ 42 ]


The second quantitative approach uses stable isotope tags to differentially label proteins from two different complex mixtures. [ 43 ] [ 44 ]  Here, the proteins within a complex mixture are labeled isotopically first, and then digested to yield labeled peptides. The labeled mixtures are then combined, the peptides separated by multidimensional liquid chromatography and analyzed by tandem mass spectrometry. Isotope coded affinity tag (ICAT) reagents are the widely used isotope tags. In this method, the cysteine residues of proteins get covalently attached to the ICAT reagent, thereby reducing the complexity of the mixtures omitting the non-cysteine residues.


Quantitative proteomics  using stable isotopic tagging is an increasingly useful tool in modern development. Firstly, chemical reactions have been used to introduce tags into specific sites or proteins for the purpose of probing specific protein functionalities. The isolation of phosphorylated peptides has been achieved using isotopic labeling and selective chemistries to capture the fraction of protein among the complex mixture. Secondly, the ICAT technology was used to differentiate between partially purified or purified macromolecular complexes such as large RNA polymerase II pre-initiation complex and the proteins complexed with yeast transcription factor. Thirdly, ICAT labeling was recently combined with chromatin isolation to identify and quantify chromatin-associated proteins. Finally ICAT reagents are useful for proteomic profiling of cellular organelles and specific cellular fractions. [ 42 ]


Another quantitative approach is the accurate mass and time (AMT) tag approach developed by  Richard D. Smith  and coworkers at  Pacific Northwest National Laboratory . In this approach, increased throughput and sensitivity is achieved by avoiding the need for tandem mass spectrometry, and making use of precisely determined separation time information and highly accurate mass determinations for peptide and protein identifications.


Affinity proteomics uses antibodies or other affinity reagents (such as oligonucleotide-based aptamers) as protein-specific detection probes. [ 45 ]  Currently this method can interrogate several thousand proteins, typically from biofluids such as plasma, serum or cerebrospinal fluid (CSF). A key differentiator for this technology is the ability to analyze hundreds or thousands of samples in a reasonable timeframe (a matter of days or weeks); mass spectrometry-based methods are not scalable to this level of sample throughput for proteomics analyses.


Balancing the use of mass spectrometers in proteomics and in medicine is the use of protein micro arrays. The aim behind protein micro arrays is to print thousands of protein detecting features for the interrogation of biological samples. Antibody arrays are an example in which a host of different antibodies are arrayed to detect their respective antigens from a sample of human blood. Another approach is the arraying of multiple protein types for the study of properties like protein-DNA, protein-protein and protein-ligand interactions. Ideally, the functional proteomic arrays would contain the entire complement of the proteins of a given organism. The first version of such arrays consisted of 5000 purified proteins from yeast deposited onto glass microscopic slides. Despite the success of first chip, it was a greater challenge for protein arrays to be implemented. Proteins are inherently much more difficult to work with than DNA. They have a broad dynamic range, are less stable than DNA and their structure is difficult to preserve on glass slides, though they are essential for most assays. The global ICAT technology has striking advantages over protein chip technologies. [ 42 ]


This is a promising and newer microarray application for the diagnosis, study and treatment of complex diseases such as cancer. The technology merges  laser capture microdissection (LCM)  with micro array technology, to produce reverse-phase protein microarrays. In this type of microarrays, the whole collection of protein themselves are immobilized with the intent of capturing various stages of disease within an individual patient. When used with LCM, reverse phase arrays can monitor the fluctuating state of proteome among different cell population within a small area of human tissue. This is useful for profiling the status of cellular signaling molecules, among a cross-section of tissue that includes both normal and cancerous cells. This approach is useful in monitoring the status of key factors in normal prostate epithelium and invasive prostate cancer tissues.  LCM then dissects these tissue and protein lysates were arrayed onto nitrocellulose slides, which were probed with specific antibodies. This method can track all kinds of molecular events and can compare diseased and healthy tissues within the same patient enabling the development of treatment strategies and diagnosis. The ability to acquire proteomics snapshots of neighboring cell populations, using reverse-phase microarrays in conjunction with LCM has a number of applications beyond the study of tumors. The approach can provide insights into normal physiology and pathology of all the tissues and is invaluable for characterizing developmental processes and anomalies. [ 42 ]


Recent advancements in  bioorthogonal chemistry  have revealed applications in protein analysis. The extension of using organic molecules to observe their reaction with proteins reveals extensive methods to tag them.  Unnatural amino acids  and various  functional groups  represent new growing technologies in proteomics.


Specific biomolecules that are capable of being metabolized in cells or tissues are inserted into proteins or glycans. The molecule will have an affinity tag, modifying the protein allowing it to be detected. Azidohomoalanine (AHA) utilizes this affinity tag via incorporation with Met-t-RNA synthetase to incorporate into proteins. This has allowed AHA to assist in determine the identity of newly synthesized proteins created in response to  perturbations  and to identify proteins secreted by cells. [ 46 ]


Recent studies [ 47 ]  using  ketones  and  aldehydes  condensations show that they are best suited for in vitro or  cell surface labeling . However, using ketones and aldehydes as bioorthogonal reporters revealed slow kinetics indicating that while effective for labeling, the concentration must be high.


Certain proteins can be detected via their reactivity to  azide groups .  Non-proteinogenic amino acids  can bear azide groups which react with phosphines in  Staudinger ligations . This reaction has already been used to label other biomolecules in living cells and animals. [ 48 ]


The bioorthoganal field is expanding and is driving further applications within proteomics. It is worthwhile noting the limitations and benefits. Rapid reactions can create bioconjuctions and create high concentrations with low amounts of reactants. Contrarily slow kinetic reactions like aldehyde and ketone condensation while effective require a high concentration making it cost inefficient.


One major development to come from the study of human genes and proteins has been the identification of potential new drugs for the treatment of disease. This relies on  genome  and  proteome  information to identify proteins associated with a disease, which computer software can then use as targets for new drugs. For example, if a certain protein is implicated in a disease, its 3D structure provides the information to design drugs to interfere with the action of the protein. A molecule that fits the active site of an enzyme, but cannot be released by the enzyme, inactivates the enzyme. This is the basis of new drug-discovery tools, which aim to find new drugs to inactivate proteins involved in disease. As genetic differences among individuals are found, researchers expect to use these techniques to develop personalized drugs that are more effective for the individual. [ 49 ]


Proteomics is also used to reveal complex plant-insect interactions that help identify candidate genes involved in the defensive response of plants to herbivory. [ 50 ] [ 51 ] [ 52 ]


A branch of proteomics called  chemoproteomics  provides numerous tools and techniques to detect protein targets of drugs. [ 53 ]


Interaction proteomics is the analysis of protein interactions from scales of binary interactions to proteome- or network-wide.  Most proteins function via  protein–protein interactions , and one goal of interaction proteomics is to  identify binary protein interactions ,  protein complexes , and  interactomes .


Several methods are available to  probe protein–protein interactions . While the most traditional method is yeast  two-hybrid analysis , a powerful emerging method is  affinity purification  followed by  protein mass spectrometry  using  tagged protein baits . Other methods include  surface plasmon resonance  (SPR), [ 54 ] [ 55 ]   protein microarrays ,  dual polarisation interferometry ,  microscale thermophoresis ,  kinetic exclusion assay , and experimental methods such as  phage display  and  in silico  computational methods.


Knowledge of protein-protein interactions is especially useful in regard to  biological networks  and  systems biology , for example in  cell signaling  cascades and  gene regulatory networks  (GRNs, where knowledge of  protein-DNA interactions  is also informative). Proteome-wide analysis of protein interactions, and integration of these interaction patterns into larger  biological networks , is crucial towards understanding  systems-level biology . [ 56 ] [ 57 ]


Expression proteomics includes the analysis of  protein expression  at a larger scale. It helps identify main proteins in a particular sample, and those proteins differentially expressed in related samples—such as diseased vs. healthy tissue. If a protein is found only in a diseased sample then it can be a useful drug target or diagnostic marker. Proteins with the same or similar expression profiles may also be functionally related. There are technologies such as 2D-PAGE and  mass spectrometry  that are used in expression proteomics. [ 58 ]


The  National Institutes of Health  has defined a biomarker as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention." [ 59 ] [ 60 ]


Understanding the proteome, the structure and function of each protein and the complexities of protein–protein interactions are critical for developing the most effective diagnostic techniques and disease treatments in the future. For example, proteomics is highly useful in the identification of candidate biomarkers (proteins in body fluids that are of value for diagnosis), identification of the bacterial antigens that are targeted by the immune response, and identification of possible immunohistochemistry markers of infectious or neoplastic diseases. [ 61 ]


An interesting use of proteomics is using specific protein biomarkers to diagnose disease. A number of techniques allow to test for proteins produced during a particular disease, which helps to diagnose the disease quickly. Techniques include  western blot ,  immunohistochemical staining ,  enzyme linked immunosorbent assay  (ELISA) or  mass spectrometry . [ 40 ] [ 62 ]   Secretomics , a subfield of proteomics that studies  secreted proteins  and secretion pathways using proteomic approaches, has recently emerged as an important tool for the discovery of biomarkers of disease. [ 63 ]


In  proteogenomics , proteomic technologies such as  mass spectrometry  are used for improving  gene annotations . Parallel analysis of the genome and the proteome facilitates discovery of post-translational modifications and proteolytic events, [ 64 ]  especially when comparing multiple species (comparative proteogenomics). [ 65 ]


Structural proteomics includes the analysis of protein structures at large-scale. It compares protein structures and helps identify functions of newly discovered genes. The structural analysis also helps to understand that where drugs bind to proteins and also shows where proteins interact with each other. This understanding is achieved using different technologies such as X-ray crystallography and NMR spectroscopy. [ 58 ]


Much proteomics data is collected with the help of high throughput technologies such as  mass spectrometry  and microarray. It would often take weeks or months to analyze the data and perform comparisons by hand. For this reason, biologists and chemists are collaborating with computer scientists and mathematicians to create programs and pipeline to computationally analyze the protein data. Using  bioinformatics  techniques, researchers are capable of faster analysis and data storage. A good place to find lists of current programs and databases is on the  ExPASy  bioinformatics resource portal. The applications of bioinformatics-based proteomics include medicine, disease diagnosis, biomarker identification, and many more.


Mass spectrometry and microarray produce peptide fragmentation information but do not give identification of specific proteins present in the original sample. Due to the lack of specific protein identification, past researchers were forced to decipher the peptide fragments themselves. However, there are currently programs available for protein identification. These programs take the peptide sequences output from mass spectrometry and microarray and return information about matching or similar proteins. This is done through algorithms implemented by the program which perform alignments with proteins from known databases such as UniProt [ 66 ]  and  PROSITE [ 67 ]  to predict what proteins are in the sample with a degree of certainty.


The  biomolecular structure  forms the 3D configuration of the protein. Understanding the protein's structure aids in the identification of the protein's interactions and function. It used to be that the 3D structure of proteins could only be determined using  X-ray crystallography  and  NMR spectroscopy . As of 2017,  Cryo-electron microscopy  is a leading technique, solving difficulties with crystallization (in X-ray crystallography) and conformational ambiguity (in NMR); resolution was 2.2Å as of 2015. Now, through bioinformatics, there are computer programs that can in some cases predict and model the structure of proteins. These programs use the chemical properties of amino acids and structural properties of known proteins to predict the 3D model of sample proteins. This also allows scientists to model protein interactions on a larger scale. In addition, biomedical engineers are developing methods to factor in the flexibility of protein structures to make comparisons and predictions. [ 68 ]


Most programs available for protein analysis are not written for proteins that have undergone  post-translational modifications . [ 69 ]  Some programs will accept post-translational modifications to aid in protein identification but then ignore the modification during further protein analysis. It is important to account for these modifications since they can affect the protein's structure. In turn, computational analysis of post-translational modifications has gained the attention of the scientific community. The current post-translational modification programs are only predictive. [ 70 ]  Chemists, biologists and computer scientists are working together to create and introduce new pipelines that allow for analysis of post-translational modifications that have been experimentally identified for their effect on the protein's structure and function.


One example of the use of bioinformatics and the use of computational methods is the study of protein biomarkers. Computational predictive models [ 71 ]  have shown that extensive and diverse feto-maternal protein trafficking occurs during pregnancy and can be readily detected non-invasively in maternal whole blood. This computational approach circumvented a major limitation, the abundance of maternal proteins interfering with the detection of  fetal proteins , to fetal proteomic analysis of maternal blood. Computational models can use fetal gene transcripts previously identified in maternal  whole blood  to create a comprehensive proteomic network of the term  neonate . Such work shows that the fetal proteins detected in pregnant woman's blood originate from a diverse group of tissues and organs from the developing fetus. The proteomic networks contain many  biomarkers  that are proxies for development and illustrate the potential clinical application of this technology as a way to monitor normal and abnormal fetal development.


An information-theoretic framework has also been introduced for  biomarker  discovery, integrating biofluid and tissue information. [ 72 ]  This new approach takes advantage of functional synergy between certain biofluids and tissues with the potential for clinically significant findings not possible if tissues and biofluids were considered individually. By conceptualizing tissue-biofluid as information channels, significant biofluid proxies can be identified and then used for the guided development of clinical diagnostics. Candidate biomarkers are then predicted based on information transfer criteria across the tissue-biofluid channels. Significant biofluid-tissue relationships can be used to prioritize clinical validation of biomarkers. [ 72 ]


A number of emerging concepts have the potential to improve the current features of proteomics. Obtaining absolute quantification of proteins and monitoring post-translational modifications are the two tasks that impact the understanding of protein function in healthy and diseased cells. Further, the throughput and sensitivity of proteomic assays, often measured as samples analyzed per day and depth of proteome coverage, respectively, have driven development of cutting-edge instrumentation and methodologies. [ 73 ]  For many cellular events, the protein concentrations do not change; rather, their function is modulated by post-translational modifications (PTM). Methods of monitoring PTM are an underdeveloped area in proteomics. Selecting a particular subset of protein for analysis substantially reduces protein complexity, making it advantageous for diagnostic purposes where blood is the starting material. Another important aspect of proteomics, yet not addressed, is that proteomics methods should focus on studying proteins in the context of the environment. The increasing use of chemical cross-linkers, introduced into living cells to fix protein-protein, protein-DNA and other interactions, may ameliorate this problem partially. The challenge is to identify suitable methods of preserving relevant interactions. Another goal for studying proteins is development of more sophisticated methods to image proteins and other molecules in living cells and real-time. [ 42 ]


Advances in quantitative proteomics would clearly enable more in-depth analysis of cellular systems. [ 56 ] [ 57 ]  Another research frontier is the analysis of single cells, [ 74 ] [ 75 ]  and protein covariation across single cells [ 76 ]  which reflects biological processes such as protein complex formation, immune functions, [ 77 ]  as well as cell cycle and priming of cancer cells for drug resistance [ 78 ]  Biological systems are subject to a variety of perturbations ( cell cycle ,  cellular differentiation ,  carcinogenesis ,  environment (biophysical) , etc.).  Transcriptional  and  translational  responses to these perturbations results in functional changes to the proteome implicated in response to the stimulus. Therefore, describing and quantifying proteome-wide changes in protein abundance is crucial towards understanding biological phenomenon more  holistically , on the level of the entire system. In this way, proteomics can be seen as complementary to  genomics ,  transcriptomics ,  epigenomics ,  metabolomics , and other  -omics  approaches in integrative analyses attempting to define biological  phenotypes  more comprehensively. As an example,  The Cancer Proteome Atlas  provides quantitative protein expression data for ~200 proteins in over 4,000 tumor samples with matched transcriptomic and genomic data from  The Cancer Genome Atlas . [ 79 ]  Similar datasets in other cell types, tissue types, and species, particularly using deep shotgun mass spectrometry, will be an immensely important resource for research in fields like  cancer biology ,  developmental  and  stem cell  biology,  medicine , and  evolutionary biology .


Characterizing the human plasma proteome has become a major goal in the proteomics arena, but it is also the most challenging proteomes of all human tissues. [ 80 ]  It contains immunoglobulin, cytokines, protein hormones, and secreted proteins indicative of infection on top of resident, hemostatic proteins. It also contains tissue leakage proteins due to the blood circulation through different tissues in the body. The blood thus contains information on the physiological state of all tissues and, combined with its accessibility, makes the blood proteome invaluable for medical purposes. It is thought that characterizing the proteome of blood plasma is a daunting challenge.


The depth of the plasma proteome encompasses a dynamic range of more than 10 10  between the highest abundant protein (albumin) and the lowest (some cytokines) and is thought to be one of the main challenges for proteomics. [ 81 ]  Temporal and spatial dynamics further complicate the study of human plasma proteome. The turnover of some proteins is quite faster than others and the protein content of an artery may substantially vary from that of a vein. All these differences make even the simplest proteomic task of cataloging the proteome seem out of reach. To tackle this problem, priorities need to be established. Capturing the most meaningful subset of proteins among the entire proteome to generate a diagnostic tool is one such priority. Secondly, since cancer is associated with enhanced glycosylation of proteins, methods that focus on this part of proteins will also be useful. Again: multiparameter analysis best reveals a pathological state. As these technologies improve, the disease profiles should be continually related to respective gene expression changes. [ 42 ]  Due to the above-mentioned problems plasma proteomics remained challenging. However, technological advancements and continuous developments seem to result in a revival of plasma proteomics as it was shown recently by a technology called plasma proteome profiling. [ 82 ]  Due to such technologies researchers were able to investigate inflammation processes in mice, the heritability of plasma proteomes as well as to show the effect of such a common life style change like weight loss on the plasma proteome. [ 83 ] [ 84 ] [ 85 ]


Numerous journals are dedicated to the field of proteomics and related areas. Note that journals dealing with  proteins  are usually more focused on structure and function while  proteomics  journals are more focused on the large-scale analysis of whole proteomes or at least large sets of proteins. Some relevant proteomics journals are listed below (with their publishers).


The  Psychiatric Genomics Consortium  ( PGC ) is an international  consortium  of scientists dedicated to conducting  meta- and mega-analyses of genomic-wide genetic data , with a focus on  psychiatric disorders . It is the largest  psychiatric  consortium ever created, [ 1 ]  including over 800 researchers from 38 countries as of 2019. [ 2 ]  Its goal is to generate information about the genetics of psychiatric conditions that will be "actionable", that is, "genetic findings whose biological implications can be used to improve diagnosis, develop rational therapeutics, and craft mechanistic approaches to primary prevention". [ 1 ]  The consortium makes the main findings from its research freely available for use by other researchers. [ 3 ]


The PGC was founded in early 2007, originally as the  Psychiatric Genome Wide Association Consortium . [ 2 ] [ 4 ]  One of its founders was  Patrick F. Sullivan  ( UNC School of Medicine ), who now serves as its lead  principal investigator . [ 5 ]  It was initially a branch of the Genetic Association Information Network, a  public-private partnership  aimed at researching the genetics of human disorders in general. [ 6 ]


For its first four years of existence, the PGC focused on  autism spectrum disorder ,  attention-deficit hyperactivity disorder ,  bipolar disorder ,  major depressive disorder , and  schizophrenia . It also initially focused only on finding common  single nucleotide polymorphisms  that were associated with psychiatric disorders. Since then, it has expanded its scope to include other disorders, as well as less common forms of  genetic variation  such as  copy number variation . [ 2 ]


Research from the PGC has shed light on the  genetic architecture  of psychiatric disorders generally, as well as demonstrating the viability of the  genome-wide association  approach for specific disorders such as schizophrenia and bipolar disorder. The consortium has also identified 108  genetic loci  that are consistently associated with schizophrenia. [ 6 ]  In addition, its findings have pointed to significant  pleiotropy  across psychiatric disorders, with many common  alleles  influencing the risk of multiple such disorders. [ 4 ]


Psychosocial genomics  ( PG ) is a field of research first proposed by Ernest L. Rossi in 2002. [ 1 ]  PG examines the modulation of gene expression in response to psychological, social and cultural experiences. [ 2 ] [ 3 ]  Independent research shows that the experience of novelty, [ 4 ]  environmental enrichment [ 5 ] [ 6 ]  and exercise [ 7 ] [ 8 ] [ 9 ]  facilitates activity and experience dependent gene expression and brain plasticity as well as stem cell healing processes. [ 10 ] [ 11 ]


This is a top-down approach – from mind to body – that modulates the role of  gene expression  and  brain plasticity  in the development of human consciousness which can be perceived as the completion, or dynamic complement, of the bottom-up approach – direct sensorial and biological responses – as proposed by the  ENCODE  consortium. [ 12 ] [ 13 ]


PG utilizes various methods and approaches derived from genomics, neuroscience and culturomics. [ 14 ] [ 15 ]  These include DNA microarrays, and computational analysis with the GSEA database. [ 16 ]


P 3 G  ( Public Population Project in Genomics   and Society ) is a  not-for-profit   international consortium  dedicated to facilitating collaboration between researchers and biobanks working in the area of  human population genomics . [ 1 ]
P 3 G is member-based and composed of experts from the different disciplines in the areas of and related to genomics, including  epidemiology ,  law ,  ethics ,  technology ,  biomolecular science , etc.
P 3 G and its members are committed to a philosophy of information sharing with the goal of supporting researchers working in areas that will improve the  health  of people around the world.


P 3 G is a  not-for-profit organization  with members from over 40 countries. Membership falls under two different categories: Institutional and Individual. Institutional members have the right to elect and vote the  board of directors  of P³G, and all members are eligible for office. [ 2 ]


P 3 G is  headquartered  in the McGill University Genome Quebec Innovation Centre, Montreal ( Canada ).


P 3 G works with its members and other experts to develop tools, methods and resources designed to optimize and harmonize the infrastructures of biobanks and research projects in the areas of  population genomics ,  epidemiology  and the  environment . The P 3 G site is completely free and accessible, and all documents, web sites and tools included on the site are non-commercial and open source. Such tools include:


Toolkit  - provides the epidemiological, ethical, statistical and IT instruments necessary to the access and use of data. The aim of this platform is to create a one-stop location and open access environment, where key documents are accessible to the research community. The TOOLKIT currently contains over 80 tools across five categories (Epidemiology and Biostatistics, Sample collection and processing, Data collection and processing, GE3LS, other tools).


Lifespan  - an open access web platform offering users a step-by-step approach for the development and maintenance of a biobank or a research project within a definitive funding timeline.


HUB  - provides an online agora for all those interested in genetic and genomic research, allowing for ongoing discussion, exchange and collaboration on research projects.


Training  - provides researchers in the biobanking field with pertinent and up-to-date training opportunities


Catalogs  - currently provide information on large population-based biobanks. Over 160 studies, spanning all continents, are presently listed in the database, and this index is regularly updated and curated to include relevant studies.


In addition, P 3 G bring its members together at  meetings  in different cities around the world. These gatherings provide members with a unique opportunity to identify and address the challenges the community faces, share innovative strategies and work on new research tools.
P 3 G Meetings have been held in the following cities: [ 3 ]


The IPAC (International Policy interoperability and data
Access Clearinghouse) resource both promotes the interoperability of international norms and facilitates the sharing of clinical and research data. Building on the expertise of P 3 G, the IPAC offers a “one-stop” service for national and international collaborative research projects, along with normative tools and frameworks that respect the laws and regulations of each country while facilitating access.  http://www.p3g.org/ipac


IPAC Researchers


Founded on the experience of the P 3 G Research Platforms, the IPAC team includes the international expertise of ELSI (ethical/legal/social) research professionals.


Domains: Regenerative Medicine; Gene Therapy; Cancer; Paediatrics; Incompetent Adults; Deceased Persons; Rare Diseases; Bioethics/Law.


Issues: Biobanking; Consent; Access (Data/Samples); Commercialization; Confidentiality/Privacy; Research Ethics; Governance.


Proficiency in: English; French; Spanish; Arabic; Mandarin.


IPAC Services:


I. Data/Sample Collection - ELSI Interoperability


IPAC offers the following interoperability services:


II. DACO - Review of Data and Samples Access Request/Authorization and Compliance


III. P 3 G DataTrust


Due to concerns surrounding the return of clinically significant results to research participants in the context of translational projects, P 3 G has developed the DataTrust (DT) service. This service supports the process of re-contacting participants and returning individual-level results in translational research projects, when appropriate. The DataTrust (DT) proposes to provide P 3 G as an honest and independent, third party broker to act as the key holder of personal information and associated unique ID (codes). P 3 G-DT safeguards the independence of the research project team, while enhancing ethical compliance with privacy and confidentiality standards in the research setting.


P 3 G was incorporated in 2004 under the leadership of  Bartha Knoppers  and  Dr. Thomas Hudson , and was established by and for researchers working in the area of population genetics. P 3 G is funded by  Genome Quebec  and  Genome Canada , as well as other funding agencies. [ 4 ]


The term  radiogenomics  is used in two contexts: either to refer to the study of genetic variation associated with response to radiation (radiation genomics) or to refer to the correlation between cancer imaging features and gene expression (imaging genomics).


In radiation genomics, radiogenomics is used to refer to the study of  genetic variation  associated with response to  radiation therapy . Genetic variation, such as  single nucleotide polymorphisms , is studied in relation to a cancer patient's risk of developing toxicity following  radiation therapy . [ 1 ] [ 2 ] [ 3 ]     It is also used in the context of studying the  genomics  of tumor response to  radiation therapy . [ 4 ] [ 5 ]


The term radiogenomics was coined in 2002 by Andreassen et al. (2002) [ 6 ]   as an analogy to  pharmacogenomics , which studies the genetic variation associated with drug responses. See also West et al. (2005) [ 7 ]   and Bentzen (2006). [ 8 ]


In 2009, [ 9 ] [ 10 ]  a Radiogenomics Consortium (RGC) was established to facilitate and promote multi-centre collaboration of researchers linking genetic variants with response to radiation therapy. The Radiogenomics Consortium ( http://epi.grants.cancer.gov/radiogenomics/ ) is a Cancer Epidemiology Consortium supported by the Epidemiology and Genetics Research Program of the National Cancer Institute of the National Institutes of Health. [ 11 ]  RGC researchers have completed numerous clinical studies that identified genetic variants associated with radiation toxicities in patients with prostate, breast, lung, head and neck, and other cancers.


Radiological images are used to diagnose disease on a large scale: tissue imaging correlates with tissue  pathology .  The addition of genomic data including  DNA microarrays ,  miRNA ,  RNA-Seq  allows new correlations to be made between cellular genomics and tissue-scale imaging.


Recurrence Index  (RI) in genomics denotes the number of mutations per kilobase of a given genomic locus of a patient carrying particular mutations. RI represents a patient level recurrence frequency estimated for somatic mutations. [ 1 ]


Reduced representation bisulfite sequencing  ( RRBS ) is an efficient and high-throughput technique for analyzing the genome-wide  methylation  profiles on a single nucleotide level.  It combines  restriction enzymes  and  bisulfite sequencing  to enrich for areas of the genome with a high  CpG  content. Due to the high cost and depth of  sequencing  to analyze methylation status in the entire genome, Meissner et al. developed this technique in 2005 to reduce the amount of nucleotides required to sequence to 1% of the genome. [ 1 ]   The fragments that comprise the reduced genome still include the majority of  promoters , as well as regions such as  repeated sequences  that are difficult to profile using conventional bisulfite sequencing approaches. [ 2 ]


RRBS uniquely uses a specific restriction enzyme to enrich for CpGs. MspI digestion, or any restriction enzyme that recognizes CpG's and cuts them, produces only fragments with CG’s at the end. [ 2 ]  This approach enriches for CpG regions of the genome, so it can decrease the amount of sequencing required as well as decrease the cost. [ 2 ]  This technique is cost-effective especially when focusing on common CpG regions.


Only a low sample concentration, between 10-300 ng, is required for accurate data analysis. [ 2 ]  This technique can be employed when there is a lack of precious sample. Another positive aspect is that fresh or live samples are not required. Formalin-fixed and paraffin-embedded inputs can also be used. [ 2 ]


In the specific protocol steps, there are also some limitations. MspI digestion covers the majority, but not all the CG regions in the genome. [ 2 ]  Some CpG’s are missed. Missing CpG’s can also occur since this protocol is only a representative sampling of the genome. [ 2 ]  Some regions thus have lower coverage. Other variations of this protocol use alternative enzymes. [ 4 ]


During the PCR portion of the protocol, a non-proofreading polymerase must be used as a proof-reading enzyme would stop at uracil residues found in the ssDNA template. [ 1 ]   Using a polymerase that does not proof-read can also lead to increase PCR sequencing errors. [ 1 ]


Bisulfite sequencing only converts single-stranded DNA (ssDNA). Complete bisulfite conversion requires thorough denaturation and absence of re-annealed double stranded DNA (dsDNA). [ 1 ]  Easy protocol steps have been shown to drive complete denaturation. Ensuring the usage of small fragments via shearing or digestion, fresh reagents, and sufficient denaturing time is crucial for complete denaturing  [ 5 ]  Another suggested technique is to carry out the bisulfite reaction at 95 °C although DNA degradation also occurs at high temperatures. [ 5 ]  In the first hour of bisulfite reaction, it is predicted that less than 90% of the sample DNA is lost to degradation  [ 6 ]  A balance between high temperature and low temperature is required to ensure complete denaturation and decreased DNA degradation. Usage of reagents, like urea, that prevent dsDNA from forming can also be employed. [ 5 ]  With contamination of dsDNA, it can be difficult to accurately computate the data. [ 1 ]  When an unconverted cytosine is observed, it is challenging to differentiate between lack of methylation and an artifact. [ 1 ]


The significance of this technique is it allows for the sequencing of methylated areas that can't be properly profiled using conventional bisulfite sequencing techniques.  Current sequencing technologies are limited in regards to profiling areas of repeated sequences. [ 7 ]   This is unfortunate in regards to methylation studies, as these repeated sequences often contain methylated cytosines.  This is especially limiting for studies involving profiling  cancer  genomes, as a loss of methylation in this repeated sequences is observed in many cancer types. [ 8 ]   RRBS eliminates the problems encountered due to these large areas of repeated sequences and thus lets these regions be more fully annotated.


Aberrant methylation has been observed in cancer. [ 9 ]  In cancer, hypermethylation as well as hypomethylation has been seen in tumors. [ 9 ]  Since RRBS is highly sensitive, this technique can be used to quickly look at aberrant methylation in cancer. [ 10 ]  If samples from the patient's tumor and normal cells can be obtained, a comparison between these two cell types can be observed. [ 2 ] [ 10 ]   A profile of the overall methylation can be produced quite rapidly. [ 2 ]  This technique can rapidly determine the overall methylation status of cancer genomes which is cost and time effective.


Stage-specific changes can be observed in all living organisms. Modifications in overall methylation levels via reduced representation bisulfite sequencing can be useful in developmental biology.


Results compared between RRBS and MethylC-seq are highly concordant with one another. [ 11 ]  Naturally, MethylC-seq has a greater genome-wide coverage of CpGs compared to RRBS, but RRBS has a greater coverage on CpG islands. [ 11 ]
One of the other most commonly used techniques for profiling methylation is MeDiP-Seq.  This technique is done by immunopreciptiation of methylated cytosines and subsequent sequencing. [ 11 ]   RRBS has a greater resolution compared to this technique, as MeDip-Seq is limited to 150 base pairs compared the one nucleotide resolution of RRBS. [ 11 ]   Bisulfite methods, such as used by RRBS, were also found more accurate than enrichment based, such as MeDip-Seq. [ 7 ]
The data obtained on RRBS and the Illumina Infinium methylation are highly comparable, with a Pearson correlation of 0.92. [ 7 ]   The data for both platforms are also directly comparable as both use an absolute measurement of DNA.


Finally, Anchor-Based Bisulfite Sequencing (ABBS) was developed by Ben Delatte's group at Active Motif. This technology uses specialized primers that capture DNA methylation allowing for increased coverage (approx. 10x more than WGBS) and lowering sequencing costs. They also showed that ABBS is not as restricted as RRBS and can be used as an alternative for MeDIP-seq while maintaining base-resolution. [ 12 ]


A  reference genome  (also known as a  reference assembly ) is a digital  nucleic acid sequence  database, assembled by scientists as a representative example of the  set of genes  in one idealized individual organism of a species.  As they are assembled from the sequencing of  DNA  from a number of individual donors, reference  genomes  do not accurately represent the set of genes of any single individual organism.  Instead, a reference provides a  haploid  mosaic of different DNA sequences from each donor. For example, one of the most recent human reference genomes, assembly  GRCh38/hg38 , is derived from >60  genomic clone libraries . [ 1 ]  There are reference genomes for multiple species of  viruses ,  bacteria ,  fungus ,  plants , and  animals . Reference genomes are typically used as a guide on which new genomes are built, enabling them to be assembled much more quickly and cheaply than the initial  Human Genome Project . Reference genomes can be accessed online at several locations, using dedicated browsers such as  Ensembl  or  UCSC Genome Browser . [ 2 ]


The length of a genome can be measured in multiple different ways.


A simple way to measure genome length is to count the number of base pairs in the assembly. [ 3 ]


The  golden path  is an alternative measure of length that omits redundant regions such as  haplotypes  and  pseudo autosomal regions . [ 4 ] [ 5 ]  It is usually constructed by layering sequencing information over a physical map to combine scaffold information. It is a 'best estimate' of what the  genome  will look like and typically includes gaps, making it longer than the typical base pair assembly. [ 6 ]


Reference genomes assembly requires reads overlapping, creating  contigs , which are contiguous DNA regions of  consensus sequences . [ 7 ]  If there are gaps between contigs, these can be filled by  scaffolding , either by contigs amplification with PCR and sequencing or by  Bacterial Artificial Chromosome (BAC)  cloning. [ 8 ] [ 7 ]  Filling these gaps is not always possible, in this case multiple scaffolds are created in a reference assembly. [ 9 ]  Scaffolds are classified in 3 types: 1) Placed, whose chromosome, genomic coordinates and orientations are known; 2) Unlocalised, when only the chromosome is known but not the coordinates or orientation; 3) Unplaced, whose chromosome is not known. [ 10 ]


The number of  contigs  and  scaffolds , as well as their average lengths are relevant parameters, among many others, for a reference genome assembly quality assessment since they provide information about the continuity of the final mapping from the original genome. The smaller the number of scaffolds per chromosome, until a single scaffold occupies an entire chromosome, the greater the continuity of the genome assembly. [ 11 ] [ 12 ] [ 13 ]  Other related parameters are  N50  and  L50 . N50 is the length of the contigs/scaffolds in which the 50% of the assembly is found in fragments of this length or greater, while L50 is the number of contigs/scaffolds whose length is N50. The higher the value of N50, the lower the value of L50, and vice versa, indicating high continuity in the assembly. [ 14 ] [ 15 ] [ 16 ]


The human and mouse reference genomes are maintained and improved by the  Genome Reference Consortium  (GRC), a group of fewer than 20 scientists from a number of genome research institutes, including the  European Bioinformatics Institute , the  National Center for Biotechnology Information , the  Sanger Institute  and  McDonnell Genome Institute  at  Washington University in St. Louis .  GRC continues to improve reference genomes by building new alignments that contain fewer gaps, and fixing misrepresentations in the sequence.


The original human reference genome was derived from thirteen anonymous volunteers from  Buffalo, New York . Donors were recruited by advertisement in  The Buffalo News , on Sunday, March 23, 1997. The first ten male and ten female volunteers were invited to make an appointment with the project's  genetic counselors  and donate blood from which DNA was extracted. As a result of how the DNA samples were processed, about 80 percent of the reference genome came from eight people and one male, designated  RP11 , accounts for 66 percent of the total. The  ABO blood group system  differs among humans, but the human reference genome contains only an  O allele , although the others are  annotated . [ 17 ] [ 18 ] [ 19 ] [ 20 ] [ 21 ]


As the cost of  DNA sequencing  falls, and new  full genome sequencing  technologies emerge, more genome sequences continue to be generated. In several cases people such as  James D. Watson  had their genome  assembled using  massive parallel DNA sequencing . [ 22 ] [ 23 ]   Comparison between the reference (assembly NCBI36/hg18) and Watson's genome revealed 3.3  million  single nucleotide polymorphism  differences, while about 1.4 percent of  his DNA could not be matched to the reference genome at all. [ 21 ] [ 22 ]  For regions where there is known to be large-scale variation, sets of alternate  loci  are assembled alongside the reference locus.


The latest human reference genome assembly, released by the  Genome Reference Consortium , was GRCh38 in 2017. [ 25 ]  Several patches were added to update it, the latest patch being GRCh38.p14, published on the 3rd of February 2022. [ 26 ] [ 27 ]  This build only has 349 gaps across the entire assembly, which implies a great improvement in comparison with the first version, which had roughly 150,000 gaps. [ 18 ]  The gaps are mostly in areas such as  telomeres ,  centromeres , and long  repetitive sequences , with the biggest gap along the long arm of the Y chromosome, a region of ~30 Mb in length (~52% of the Y chromosome's length). [ 28 ]  The number of  genomic clone libraries  contributing to the reference has increased steadily to >60 over the years, although individual  RP11  still accounts for 70% of the reference genome. [ 1 ]  Genomic analysis of this anonymous male suggests that he is of African-European ancestry. [ 1 ]  According to the GRC website, their next assembly release for the human genome (version GRCh39) is currently "indefinitely postponed". [ 29 ]


In 2022, the Telomere-to-Telomere (T2T) Consortium, [ 30 ]  an open, community-based effort, published the first completely assembled reference genome (version T2T-CHM13), without any gaps in the assembly. It did not contain a Y-chromosome until version 2.0. [ 31 ] [ 32 ]  This assembly allows for the examination of centromeric and pericentromeric sequence evolution. The consortium employed rigorous methods to assemble, clean, and validate complex repeat regions which are particularly difficult to sequence. [ 33 ]  It used ultra-long–read (>100 kb) sequencing to accurately sequence  segmental duplications . [ 34 ]


The T2T-CHM13 is sequenced from CHM13hTERT, a cell line from an essentially haploid  hydatidiform mole . "CHM" stands for "Complete Hydatidiform Mole," and "13" is its line number. "hTERT" stands for "human  Telomerase Reverse Transcriptase ". The cell line has been transfected with the TERT gene, which is responsible for maintaining telomere length and thus contributes to the  cell line's immortality . [ 35 ]  A hydatidiform mole contains two copies of the same parental genome, and thus is essentially haploid. This eliminates allelic variation and allows better sequencing accuracy. [ 34 ]


Recent genome assemblies are as follows: [ 36 ]


For much of a genome, the reference provides a good approximation of the DNA of any single individual. But in regions with high  allelic diversity , such as the  major histocompatibility complex  in humans and the  major urinary proteins  of mice, the reference genome may differ significantly from other individuals. [ 37 ] [ 38 ] [ 39 ]  Due to the fact that the reference genome is a "single" distinct sequence, which gives its utility as an index or locator of genomic features, there are limitations in terms of how faithfully it represents the human genome and its  variability . Most of the initial samples used for reference genome sequencing came from people of European ancestry. In 2010, it was found that, by  de novo  assembling genomes from African and Asian populations with the NCBI reference genome (version NCBI36), these genomes had ~5Mb sequences that did not align against any region of the reference genome. [ 40 ]


Following projects to the Human Genome Project seek to address a deeper and more diverse characerization of the human genetic variability, which the reference genome is not able to represent. The  HapMap Project , active during the period 2002 -2010, with the purpose of creating a  haplotypes  map and their most common variations among different human populations. Up to 11 populations of different ancestry were studied, such as individuals of the  Han  ethnic group from China,  Gujaratis  from India, the  Yoruba  people from Nigeria or  Japanese people , among others. [ 41 ] [ 42 ] [ 43 ] [ 44 ]  The  1000 Genomes Project , carried out between 2008 and 2015, with the aim of creating a database that includes more than 95% of the variations present in the human genome and whose results can be used in studies of association with diseases ( GWAS ) such as diabetes, cardiovascular or autoimmune diseases. A total of 26 ethnic groups were studied in this project, expanding the scope of the HapMap project to new ethnic groups such as the  Mende people  of Sierra Leone, the  Vietnamese people  or the  Bengali people . [ 45 ] [ 46 ] [ 47 ] [ 48 ]  The  Human Pangenome Project , which started its initial phase in 2019 with the creation of the Human Pangenome Reference Consortium, seeks to create the largest map of human genetic variability taking the results of previous studies as a starting point. [ 49 ] [ 50 ]


Recent mouse genome assemblies are as follows: [ 36 ]


Since the Human Genome Project was finished, multiple international projects have started, focused on assembling reference genomes for many organisms. Model organisms (e.g., zebrafish ( Danio rerio ), chicken ( Gallus gallus ),  Escherichia coli  etc.) are of special interest to the scientific community, as well as, for example, endangered species (e.g., Asian arowana ( Scleropages formosus )  or the American bison ( Bison bison )). As of August 2022, the NCBI database supports 71 886 partially or completely sequenced and assembled genomes from different species, such as 676  mammals , 590  birds  and 865  fishes . Also noteworthy are the numbers of 1796  insects  genomes, 3747  fungi , 1025  plants , 33 724  bacteria , 26 004  virus  and 2040  archaea . [ 51 ]  A lot of these species have annotation data associated with their reference genomes that can be publicly accessed and  visuali zed in genome browsers such as  Ensembl  and  UCSC Genome Browser . [ 52 ] [ 53 ]


Some examples of these international projects are: the  Chimpanzee Genome Project , carried out between 2005 and 2013 jointly by the  Broad Institute  and the  McDonnell Genome Institute  of  Washington University in St. Louis , which generated the first reference genomes for 4 subspecies of  Pan troglodytes ; [ 54 ] [ 55 ]  the  100K Pathogen Genome Project , which started in 2012 with the main goal of creating a database of reference genomes for 100 000  pathogen  microorganisms to use in public health, outbreaks detection, agriculture and environment; [ 56 ]  the  Earth BioGenome Project , which started in 2018 and aims to sequence and catalog the genomes of all the eukaryotic organisms on Earth to promote biodiversity conservation projects. Inside this big-science project there are up to 50 smaller-scale affiliated projects such as the  Africa BioGenome Project  or the  1000 Fungal Genomes Project . [ 57 ] [ 58 ] [ 59 ]


Representational difference analysis  ( RDA ) is a technique used in biological research to find sequence differences in two  genomic  or  cDNA  samples. Genomes or cDNA sequences from two samples (i.e.  cancer  sample and a normal sample) are  PCR  amplified and differences analyzed using  subtractive DNA hybridization . This technology has been further enhanced through the development of  representation oligonucleotide microarray analysis (ROMA) , which uses  array  technology to perform such analyses.  This method may also be adapted to detect DNA methylation differences, as seen in  methylation-sensitive representational difference analysis (MS-RDA) . [ 1 ]


This method relies on PCR to differentially amplify non-homologous DNA regions between digested fragments of two nearly identical DNA species, that are called 'driver' and 'tester' DNA.  Typically, tester DNA contains a sequence of interest that is non-homologous to driver DNA.  When the two species are mixed, the driver sequence is added in excess to tester.  During PCR, double stranded fragments first denature at ~95 °C and then re-anneal when subjected to the annealing temperature.  Since driver and tester sequences are nearly identical, the excess of driver DNA fragments will anneal to homologous DNA fragments from the tester species.  This blocks PCR amplification and there is no increase in homologous fragments.  However, fragments that are different between the two species will not anneal to a complementary counterpart and will be amplified by PCR.  As more cycles of RDA are performed, the pool of unique sequence fragment copies will grow faster than fragments found in both species.


The  resistome  has been used to describe to two similar yet separate concepts:


The resistome was first used to describe the resistance capabilities of bacteria preventing the effectiveness of antibiotics . [ 4 ] [ 5 ]   Although antibiotics and their accompanying antibiotic resistant genes come from natural habitats, before next-generation sequencing, most studies of antibiotic resistance had been confined to the laboratory. [ 6 ]  Increased availability of  whole-genome  and  metagenomic  next-generation sequencing techniques have revealed significant reservoirs of antibiotic resistant bacteria outside of clinical settings. [ 4 ] [ 7 ] [ 8 ] [ 9 ]  Repeated testing of soil metagenomes revealed that in urban, agricultural, and forest environments, spore-forming soil bacteria showed resistance to most major antibiotics regardless of where they'd originated. [ 4 ]  In this study, they observed nearly 200 different resistance profiles among the bacteria sequenced, indicating a diverse and robust response to the antibiotics tested regardless of their bacterial target or natural or synthetic origin. [ 4 ]  Antibiotic resistant bacteria have observed through  metagenomic  surveys in non-clinical environments such as water treatment facilities [ 5 ] [ 8 ]  and human microbiomes like the mouth. [ 10 ]  We now know that the antibiotic resistome exists in every environmental niche on Earth, and sequences from ancient permafrost reveal that antibiotic resistance has been around millennia before the introduction of human-synthesized antibiotics. [ 9 ]


The  Comprehensive Antibiotic Research Database (CARD)  was created to compile a database of resistance genes from this rapidly increasingly available bacterial genomic data. [ 7 ]  The CARD is a compilation of sequence data and identification of resistance genes in unannotated genome sequences. [ 7 ]  The database "includes bioinformatic tools that enable the identification of  antibiotic resistance  genes from whole- or partial-genome sequence data, including unannotated raw sequence assembly  contigs ”. [ 7 ]  It is a resource intended to create a better understanding of the resistome and links healthcare, environmental, agricultural datasets. [ 7 ]


The  ResistomeDB  was published in 2020 to store the Global Ocean Resistome  [ 11 ] ., based on the metagenomics samples from the  Tara Oceans Project .


A major question surrounding the environmental resistome is: How do pathogenic bacteria acquire antibiotic resistance genes from the environment (and vice versa)? To answer this, we need to consider the mechanisms of  horizontal gene transfer  (HGT) and the various opportunities for contact between environmental bacteria and human pathogens.  [ citation needed ]