If this hypothesis was true, eukaryotic DNA sequences should reflect it. When Senapathy plotted the distribution of ORF lengths in eukaryotic DNA sequences, the plot was remarkably similar to that from random DNA sequences. This plot was also a negative exponential distribution that tailed off at a maximum of about 600 bases, as with eukaryotic genes, [ 1 ] [ 22 ] [ 3 ]  which coincided exactly with the maximum length of ORFs observed in both random DNA and eukaryotic DNA sequences.


The split genes thus originated from random DNA sequences by choosing the best of the short coding segments (exons) and splicing them. The intervening intron sequences were left-over vestiges of the random sequences, and thus were earmarked to be removed by the spliceosome. These findings indicated that split genes could have originated from random DNA sequences with exons and introns as they appear in today's eukaryotic organisms. Nobel Laureate  Marshall Nirenberg , who deciphered the codons, stated that these findings strongly showed that the split gene theory for the origin of introns and the split structure of genes must be valid. [ 1 ] [ 23 ]


Blake proposed the Gilbert-Blake hypothesis in 1979 for the origin of introns and stated that Senapathy's split gene theory comprehensively explained the origin of the split gene structure. In addition, he stated that it explained several key questions including the origin of the splicing mechanism: [ 16 ]


Recent work by Senapathy, when applied to RNA, comprehensively explains the origin of the segregated form of RNA into coding and non-coding regions. It also suggests why a splicing mechanism was developed at the start of primordial evolution. He found that the distribution of reading frame lengths in a random nucleotide sequence corresponded exactly to that for the observed distribution of eukaryotic exon sizes. These were delimited by regions containing stop signals, the messages to terminate construction of the polypeptide chain, and were thus non-coding regions or introns. The presence of a random sequence was therefore sufficient to create in the primordial ancestor the segregated form of RNA observed in the eukaryotic gene structure. Moreover, the random distribution also displays a cutoff at 600 nucleotides, which suggests that the maximum size for an early polypeptide was 200 residues, again as observed in the maximum size of the eukaryotic exon. Thus, in response to evolutionary pressures to create larger and more complex genes, the RNA fragments were joined together by a splicing mechanism that removed the introns. Hence, the early existence of both introns and RNA splicing in eukaryotes appears to be very likely from a simple statistical basis. These results also agree with the linear relationship found between the number of exons in the gene for a particular protein and the length of the polypeptide chain.”


Under the split gene theory, an exon is defined by an ORF. It requires a mechanism to recognize an ORF to have originated. As an ORF is defined by a contiguous coding sequence bounded by stop codons, these stop codon ends had to be recognized by the exon-intron gene recognition system. This system could have defined the exons by the presence of a stop codon at the ends of ORFs, which should be included within the ends of the introns and eliminated by the splicing process. Thus, the introns should contain a stop codon at their ends, which would be part of the splice junction sequences.


If this hypothesis was true, the split genes of today's living organisms should contain stop codons exactly at the ends of introns. When Senapathy tested this hypothesis in the splice junctions of eukaryotic genes, he found that the vast majority of splice junctions did contain a stop codon at the end of each intron, outside of the exons. In fact, these stop codons were found to form the “canonical” GT:AG splicing sequence, with the three stop codons occurring as part of the strong consensus signals. Thus, the basic split gene theory for the origin of introns and the split gene structure led to the understanding that the splice junctions originated from the stop codons. [ 2 ]


Sequence data for only about 1,000 exon-intron junctions were available when Senapathy thought about this question. He took the data for 1,030 splice junction sequences (donors and acceptors) and counted the codons occurring at
each of the 7- base positions in the donor signal sequence [CAG:GTGAGT] and each of the possible 2-base positions in the acceptor signal [CAG:G] from the GenBank database. He found that the stop codons occurred at high frequency only at the 5th base position in the donor signal and the first base position in the acceptor signal. These positions are the* start of the intron (in fact, one base after the start) and at the end of the intron, as Senapathy had predicted. The codon counts at only these positions are shown. Even when the codons at these positions were not stop
codons, 70% of them began with the first two bases of the stop codons TA and TG [TAT = 75; TAC = 59; TGT = 70].


All three stop codons (TGA, TAA and TAG) were found after one base (G) at the start of introns. These stop codons are shown in the consensus canonical donor splice junction as AG:GT(A/G)GGT, wherein the TAA and TGA are the stop codons, and the additional TAG is also present at this position. Besides the codon CAG, only TAG, which is a stop codon, was found at the ends of introns. The canonical acceptor splice junction is shown as (C/T)AG:GT, in which TAG is the stop codon. These consensus sequences clearly show the presence of the stop codons at the ends of introns bordering the exons in all eukaryotic genes, thus providing a strong corroboration for the split gene theory. Nirenberg again stated that these observations fully supported the split gene theory for the origin of splice junction sequences from stop codons. [ 2 ] [ 24 ]


Soon after the discovery of introns by  Philip Sharp  and  Richard Roberts , it became known that mutations within splice junctions could lead to diseases. Senapathy showed that mutations in the stop codon bases (canonical bases) caused more diseases than the mutations in non-canonical bases. [ 1 ]


An intermediate stage in the process of eukaryotic RNA splicing is the formation of a lariat structure. It is anchored at an  adenosine  residue in intron between 10 and 50 nucleotides upstream of the 3' splice site. A short conserved sequence (the branch point sequence) functions as the recognition signal for the site of lariat formation. During the splicing process, this conserved sequence towards the end of the intron forms a lariat structure with the beginning of the intron. [ 25 ]  The final step of the splicing process occurs when the two exons are joined and the intron is released as a lariat RNA. [ 26 ]


Several investigators found the branch point sequences in different  organisms [ 25 ]  including yeast, human, fruit fly, rat, and plants. Senapathy found that, in all of these sequences, the codon ending at the branch point  adenosine  is consistently a stop codon. What is interesting is that two of the three stop codons (TAA and TGA) occur almost all of the time at this position.


C TAA T


C TGA T


C TAA C


CTCAC


Lariat (branch point) sequences have been identified from many different


organisms. These sequences consistently show that the codon ending in


the branching   adenosine is a stop codon, either TAA or TGA, which are shown in red.


These findings led Senapathy to propose that the branch point signal originated from stop codons. The finding that two different stop codons (TAA and TGA) occur within the lariat signal with the branching point as the third base of the stop codons corroborates this proposal. As the branching point of the lariat occurs at the last adenine of the stop codon, it is possible that the spliceosome machinery that originated for the elimination of the stop codons from the primary RNA sequence created an auxiliary stop-codon sequence signal as the lariat sequence to aid its splicing function. [ 2 ]


The small nuclear U2 RNA found in splicing complexes is thought to aid splicing by interacting with the lariat sequence. [ 27 ]  Complementary sequences for both the lariat sequence and the acceptor signal are present in a segment of only 15 nucleotides in U2 RNA. Further, the U1 RNA has been proposed to function as a guide in splicing to identify the precise donor splice junction by complementary base-pairing. The conserved regions of the U1 RNA thus include sequences complementary to the stop codons. These observations enabled Senapathy to predict that stop codons had operated in the origin of not only the splice-junction signals and the lariat signal, but also some small nuclear RNAs.


Senapathy proposed that the gene-expression regulatory sequences (promoter and poly-A addition site sequences) also could have originated from stop codons. A conserved sequence, AATAAA, exists in almost every gene a short distance downstream from the end of the protein-coding message and serves as a signal for the addition of poly(A) in the mRNA copy of the gene. [ 28 ]  This poly(A) sequence signal contains a stop codon, TAA. A sequence shortly downstream from this signal, thought to be part of the complete poly(A) signal, also contains the TAG and TGA stop codons.


Eukaryotic RNA-polymerase-II-dependent  promoters  can contain a TATA box (consensus sequence TATAAA), which contains the stop codon TAA. Bacterial promoter elements at ~10 bases exhibits a TATA box with a consensus of TATAAT (which contains the stop codon TAA), and at -35 bases exhibits a consensus of TTGACA (containing the stop codon TGA). Thus, the evolution of the whole RNA processing mechanism seems to have been influenced by the too-frequent occurrence of stop codons, thus making the stop codons the focal points for RNA processing.


CAG:G TGA GT


Senapathy discovered that stop codons occur as key parts in every genetic element in eukaryotic genes. The table and figure show that the key parts of the core promoter elements, the lariat signal, the donor and acceptor splice signals, and the poly-A addition signal consist of one or more stop codons. This finding corroborates the split gene theory's claim that the underlying reason for the complete split gene paradigm is the origin of split genes from random DNA sequences, wherein random distribution of an extremely high frequency of stop codons were used by nature to define these genetic elements.


Research based on the split gene theory sheds light on other basic questions of exons and introns. The exons of  eukaryotes  are generally short (human exons average ~120 bases, and can be as short as 10 bases) and introns are usually long (average of ~3,000 bases, and can be several hundred thousands bases long), for example genes RBFOX1, CNTNAP2, PTPRD and DLG2. Senapathy provided a plausible answer to these questions, the only explanation to date. If eukaryotic genes originated from random DNA sequences, they have to match the lengths of ORFs from random sequences, and possibly should be around 100 bases (close to the median length of ORFs in random sequence). The genome sequences of living organisms exhibit exactly the same average lengths of 120 bases for exons, and the longest exons of 600 bases (with few exceptions), which is the same length as that of the longest random ORFs. [ 1 ] [ 2 ] [ 3 ] [ 22 ]


If split genes originated in random DNA sequences, then introns would be long for several reasons. The stop codons occur in clusters leading to numerous consecutive short ORFs: longer ORFs that could be defined as exons would be rarer. Furthermore, the best of the coding sequence parameters for functional proteins would be chosen from the long ORFs in random sequence, which may occur rarely. In addition, the combination of donor and acceptor splice junction sequences within short lengths of coding sequence segments that would define exon boundaries would occur rarely in a random sequence. These combined reasons would make introns long compared to exons.


This work also explains why genomes such as the human genome have billions of bases, and why only a small fraction (~2%) codes for proteins and other regulatory elements. [ 29 ] [ 30 ]  If split genes originated from random primordial DNA sequences, they would contain a significant amount of DNA that represented by introns. Furthermore, a genome assembled from random DNA containing split genes would also include intergenic random DNA. Thus, genomes that originated from random DNA sequences had to be large, regardless of the complexity of the organism.


The observation that several organisms such as the onion (~16 billion bases [ 31 ] ) and salamander (~32 billion bases [ 32 ] ) have much larger genomes than humans (~3 billion bases [ 33 ] [ 34 ] ) while the organisms are no more complex than humans comports with the theory. Furthermore, the fact that  several organisms with smaller genomes have a similar number of genes as human, such as  C. elegans  (genome size ~100 million bases, ~19,000 genes) [ 35 ]  and  Arabidopsis thaliana  (genome size ~125 million bases, ~25,000 genes), [ 36 ]  supports the theory. The theory predicts that the introns in the split genes in these genomes could be the “reduced” (or deleted) form compared to larger genes with long introns, thus leading to reduced genomes. [ 1 ] [ 22 ]  In fact, researchers have recently proposed that these smaller genomes are actually reduced genomes. [ 37 ]


Senapathy addressed the origin of the spliceosomal machinery that edits out the introns from RNA transcripts. If the split genes had originated from random DNA, then the introns would have become an unnecessary but integral part of eukaryotic genes along with the splice junctions. The spliceosomal machinery would be required to remove them and to enable the short exons to be linearly spliced together as a contiguously coding mRNA that can be translated into a complete protein. Thus, the split gene theory argues that spliceosomal machinery exists to remove the unnecessary introns. [ 1 ] [ 2 ]


Blake states, “Work by Senapathy, when applied to RNA, comprehensively explains the origin of the segregated form of RNA into coding and noncoding regions. It also suggests why a splicing mechanism was developed at the start of primordial evolution.” [ 16 ]


Senapathy proposed a plausible mechanistic and functional rationale why the eukaryotic nucleus originated, a major question in biology. [ 1 ] [ 2 ]  If the transcripts of the split genes and the spliced mRNAs were present in a cell without a nucleus, the ribosomes would try to bind to both the un-spliced primary RNA transcript and the spliced mRNA, which would result in chaos. A boundary that separates the RNA splicing process from the mRNA translation avoids this problem. The nuclear boundary provides a clear separation of the primary RNA splicing and the mRNA translation.


These investigations thus led to the possibility that primordial DNA with essentially random sequence gave rise to the complex structure of the split genes with exons, introns and splice junctions. Cells that harbored split genes had to be complex with a nuclear cytoplasmic boundary, and must have a spliceosomal machinery. Thus, it was possible that the earliest cell was complex and eukaryotic. [ 1 ] [ 2 ] [ 3 ] [ 22 ]  Surprisingly, findings from extensive  comparative genomics  research from several organisms since 2007 overwhelmingly show that the earliest organisms could have been highly complex and eukaryotic, and could have contained complex proteins, [ 38 ] [ 39 ] [ 40 ] [ 41 ] [ 42 ] [ 43 ] [ 44 ]  as predicted by Senapathy's theory.


The spliceosome is a highly complex mechanism, containing ~200 proteins and several  SnRNPs . Collins and Penny stated, “We begin with the hypothesis that ... the spliceosome has increased in complexity throughout eukaryotic evolution. However, examination of the distribution of spliceosomal components indicates that not only was a spliceosome present in the eukaryotic ancestor but it also contained most of the key components found in today's eukaryotes. ... the last common ancestor of extant eukaryotes appears to show much of the molecular complexity seen today.” This suggests that the earliest eukaryotic organisms were complex and contained sophisticated genes and proteins. [ 45 ]


Genes with uninterrupted coding sequences that are thousands of bases long - up to 90,000 bases - that occur in many bacterial organisms [ 21 ]  were practically impossible to have occurred. However, the bacterial genes could have originated from split genes by losing introns, the only proposed way to arrive at long coding sequences. It is also a better [ clarification needed ]  way than by increasing the lengths of ORFs from short random ORFs to long ORFs by specifically removing the stop codons by mutation. [ 1 ] [ 2 ] [ 3 ]


According to the split gene theory, this process of intron loss could have happened from prebiotic random DNA. These contiguously coding genes could be tightly organized in the bacterial genomes without any introns and be more streamlined. According to Senapathy, the nuclear boundary that was required for a cell containing split genes would not be required for a cell containing only uninterrupted genes. Thus, the bacterial cells did not develop a nucleus. Based on split gene theory, the eukaryotic genomes and bacterial genomes could have independently originated from the split genes in primordial random DNA sequences.


Senapathy developed algorithms to detect donor and acceptor splice sites, exons and a complete split gene in a genomic sequence. He developed the position weight matrix (PWM) method based on the frequency of the four bases at the consensus sequences of the donor and acceptor in different organisms to identify the splice sites in a given sequence. Furthermore, he formulated the first algorithm to find the exons based on the requirement of exons to contain a donor sequence (at the 5’ end) and an acceptor sequence (at the 3’ end), and an ORF in which the exon should occur, and another algorithm to find a complete split gene. These algorithms are collectively known as the  Shapiro-Senapathy algorithm  (S&S). [ 46 ] [ 47 ]


This algorithm aids in the identification of splicing mutations that cause disease and adverse drug reactions. [ 46 ] [ 47 ]  Scientists used the algorithm to identify mutations and genes that cause cancers, inherited disorders, immune deficiency diseases and neurological disorders. It is increasingly used in clinical practice and research to find mutations in known disease-causing genes in patients and to discover novel genes that are causal of different diseases. Furthermore, it is used in defining the cryptic splice sites and deducing the mechanisms by which mutations can affect normal splicing and lead to different diseases. It is also employed in basic research.


Findings based on S&S have impacted major questions in eukaryotic biology and in human medicine. [ 48 ]


The split gene theory implies that structural features of split genes predicted from computer-simulated random sequences occur in eukaryotic split genes. This is borne out in most known split genes. The sequences exhibit a nearly perfect negative exponential distribution of ORF lengths. [ 1 ] [ 2 ] [ 22 ] [ 3 ]  With rare exceptions, eukaryotic gene exons fall within the predicted 600 base maximum.


The theory correctly predicts that exons are delimited by stop codons, especially at the 3’ ends of exons. Actually they are precisely delimited more strongly at the 3’ ends of exons and less strongly at the 5’ ends in most known genes, as predicted. [ 1 ] [ 2 ] [ 22 ] [ 3 ]  These stop codons are the most important functional parts of both splice junctions. The theory thus provides an explanation for the “conserved” splice junctions at the ends of exons and for the loss of these stop codons along with introns when they are spliced out. The theory correctly predicts that splice junctions are randomly distributed in eukaryotic DNA sequences. [ 3 ] [ 25 ] [ 46 ] [ 47 ]  The theory correctly predicts that splice junctions present in transfer RNA genes and ribosomal RNA genes,  do not contain stop codons. The lariat signal, another sequence involved in the splicing process, also contains stop codons. [ 1 ] [ 2 ] [ 3 ] [ 22 ] [ 25 ] [ 46 ] [ 47 ]


The theory correctly predicts that introns are non-coding and that they are mostly non-functional. Except for some intron sequences including the donor and acceptor splice signal sequences and branch point sequences, and possibly the intron splice enhancers that occur at the ends of introns, which aid in the removal of introns, the vast majority of introns are devoid of any functions. The theory does not exclude rare sequences within introns that could be used by the genome and the cell, especially because introns are so long.


Thus, the theory's predictions are precisely corroborated by the major elements in modern eukaryotic genomes.


Comparative analysis of the modern genome data from several living organisms found that the characteristics of split genes trace back to the earliest organisms. These organisms could have contained the split genes and complex proteins that occur in today's living organisms. [ 49 ] [ 50 ] [ 51 ] [ 52 ] [ 53 ] [ 54 ] [ 55 ] [ 56 ] [ 57 ]


Studies employing maximum likelihood analysis found that the earliest eukaryotic organisms contained the same genes as modern organisms with yet a higher intron density. [ 58 ]   Comparative genomics  of many organisms including basal eukaryotes [ 59 ]  (considered to be primitive eukaryotic organisms such as Amoeboflagellata,  Diplomonadida , and  Parabasalia ) showed that intron-rich split genes accompanied and spliceosome from modern organisms were present in their earliest forebears, and that the earliest organisms came with all the eukaryotic cellular components. [ 60 ] [ 49 ] [ 61 ] [ 62 ] [ 63 ] [ 58 ]


Short   tandem   repeat  ( STR ) analysis is a common  molecular biology  method used to compare allele repeats at specific  loci  in  DNA  between two or more samples.  A  short tandem repeat  is a  microsatellite  with repeat units that are 2 to 7 base pairs in length, with the number of repeats varying among individuals, making STRs effective for human identification purposes. [ 2 ]  This method differs from  restriction fragment length polymorphism analysis  (RFLP) since STR analysis does not cut the DNA with restriction enzymes. Instead,  polymerase chain reaction  (PCR) is employed to discover the lengths of the short tandem repeats based on the length of the PCR product.


STR analysis is a tool in  forensic analysis  that evaluates specific STR regions found on  nuclear DNA . The variable (polymorphic) nature of the STR regions that are analyzed for forensic testing intensifies the discrimination between one DNA profile and another. [ 3 ]  Scientific tools such as FBI approved  STRmix  incorporate this research technique. [ 4 ] [ 5 ]  Forensic science takes advantage of the population's variability in STR lengths, enabling scientists to distinguish one DNA sample from another. The system of  DNA profiling  used today is based on PCR and uses simple sequences [ 6 ]  or short tandem repeats (STR). This method uses highly polymorphic regions that have short repeated sequences of DNA (the most common is 4 bases repeated, but there are other lengths in use, including 3 and 5 bases). Because unrelated people almost certainly have different numbers of repeat units, STRs can be used to discriminate between unrelated individuals. These STR  loci  (locations on a chromosome) are targeted with sequence-specific primers and amplified using  PCR . The DNA fragments that result are then separated and detected using  electrophoresis . There are two common methods of separation and detection,  capillary electrophoresis  (CE) and  gel electrophoresis .


Each STR is polymorphic, but the number of alleles is very small. Typically each STR allele will be shared by around 5 - 20% of individuals. The power of STR analysis comes from looking at multiple STR loci simultaneously. [ 6 ]  The pattern of alleles can identify an individual quite accurately. Thus STR analysis provides an excellent identification tool. The more STR regions that are tested in an individual the more discriminating the test becomes. [ 6 ]  However, given 10 STR loci, it can result in a genotyping error margin of 30%, or nearly one third (1/3) of the time. [ 7 ]  Even when using 15 identifier microsatellite STR loci, they are not informative markers for inference of ancestry, a much larger set of genetic markers is needed to detect fine-scale population structure. [ 8 ]  A study claimed 30 DIP-STRs were found to be suitable for prenatal paternity testing and roughly outlining biogeographic ancestry in forensics, but more markers and multiplex panels need to be developed to promote use of this original approach. [ 9 ]


When comparing SNP and STR analysis, the use of  high-quality SNPs  has proven to be better for delineating population structure, as well as genetic relationships at the individual and population level. [ 10 ]  Using the best 15 SNPs (30 alleles) was similar to the best 4 STR loci (83 alleles), and increasing the STR made no difference, but increasing to 100 SNPs substantially increased assignment giving the highest result. Researchers found that some of the STR loci out-performed the SNP loci on a single locus basis, but combinations of SNPs outperformed the STRs based upon total number of alleles. The SNPs from a larger panel gave significantly more accurate individual genetic self-assignment compared to any combination of the STR loci. [ 10 ]


From country to country, different STR-based DNA-profiling systems are in use. In North America, systems that amplify the  CODIS  20 core loci are almost universal, whereas in the United Kingdom the  DNA-17  17 loci system (which is compatible with The  National DNA Database ) is in use. Whichever system is used, many of the STR regions used are the same. These DNA-profiling systems are based on multiplex reactions, whereby many STR regions will be tested at the same time.


The true power of STR analysis is in its statistical power of discrimination. Because the 20 loci that are currently used for discrimination in CODIS are  independently assorted  (having a certain number of repeats at one locus does not change the likelihood of having any number of repeats at any other locus), the  product rule for probabilities  can be applied. This means that, if someone has the DNA type of ABC, where the three loci were independent, we can say that the probability of having that DNA type is the probability of having type A times the probability of having type B times the probability of having type C. This has resulted in the ability to generate match probabilities of 1 in a quintillion (1x10 18 ) or more. However, DNA database searches showed much more frequent than expected false DNA profile matches. [ 11 ]  Moreover, since there are about 12 million  monozygotic twins  on Earth, the theoretical probability is not accurate.


In practice, the risk of contaminated-matching is much greater than matching a distant relative, such as contamination of a sample from nearby objects, or from left-over cells transferred from a prior test. The risk is greater for matching the most common person in the samples: Everything collected from, or in contact with, a victim is a major source of contamination for any other samples brought into a lab. For that reason, multiple control-samples are typically tested in order to ensure that they stayed clean, when prepared during the same period as the actual test samples. Unexpected matches (or variations) in several control-samples indicates a high probability of contamination for the actual test samples. In a relationship test, the full DNA profiles should differ (except for twins), to prove that a person was not matched as being related to their own DNA in another sample. [ citation needed ]


In biomedical research, STR profiles are used to authenticate cell lines. [ 12 ]  Self-generated STR profiles can be compared with databases such as CLASTR ( https://www.cellosaurus.org/cellosaurus-str-search/ ) or STRBase ( https://strbase.nist.gov/ ). In addition, self-generated primary murine cell lines cultured before the first passaging can be matched with later passages, thus ensuring the identity of the cell line.


Structural genomics  seeks to describe the  3-dimensional structure  of every protein encoded by a given  genome . This genome-based approach allows for a high-throughput method of structure determination by a combination of  experimental and modeling approaches . The principal difference between structural genomics and  traditional structural prediction  is that structural genomics attempts to determine the structure of every protein encoded by the genome, rather than focusing on one particular protein. With full-genome sequences available, structure prediction can be done more quickly through a combination of experimental and modeling approaches, especially because the availability of large number of sequenced genomes and previously solved protein structures allows scientists to model protein structure on the structures of previously solved homologs.


Because protein structure is closely linked with protein function, the structural genomics has the potential to inform knowledge of protein function. In addition to elucidating protein functions, structural genomics can be used to identify novel protein folds and potential targets for drug discovery. Structural genomics involves taking a large number of approaches to structure determination, including experimental methods using genomic sequences or modeling-based approaches based on sequence or  structural homology  to a protein of known structure or based on chemical and physical principles for a protein with no homology to any known structure.


As opposed to traditional  structural biology , the determination of a  protein structure  through a structural genomics effort often (but not always) comes before anything is known regarding the protein function. This raises new challenges in  structural bioinformatics , i.e. determining protein function from its  3D  structure.


Structural genomics emphasizes high throughput determination of protein structures. This is performed in dedicated  centers of structural genomics .


While most structural biologists pursue structures of individual proteins or protein groups, specialists in structural genomics pursue structures of proteins on a genome wide scale. This implies large-scale cloning, expression and purification. One main advantage of this approach is economy of scale. On the other hand, the scientific value of some resultant structures is at times questioned. A  Science  article from January 2006 analyzes the structural genomics field. [ 1 ]


One advantage of structural genomics, such as the  Protein Structure Initiative , is that the scientific community gets immediate access to new structures, as well as to reagents such as clones and protein. A disadvantage is that many of these structures are of proteins of unknown function and do not have corresponding publications. This requires new ways of communicating this structural information to the broader research community. The Bioinformatics core of the Joint center for structural genomics (JCSG) has recently developed a wiki-based approach namely  Open protein structure annotation network  (TOPSAN) for annotating protein structures emerging from high-throughput structural genomics centers.


One goal of structural genomics is to identify novel protein folds. Experimental methods of protein structure determination require proteins that express and/or crystallize well, which may inherently bias the kinds of proteins folds that this experimental data elucidate. A genomic, modeling-based approach such as  ab initio  modeling  may be better able to identify novel protein folds than the experimental approaches because they are not limited by experimental constraints.


Protein function depends on 3-D structure and these 3-D structures are more highly conserved than  sequences . Thus, the high-throughput structure determination methods of structural genomics have the potential to inform our understanding of protein functions.  This also has potential implications for drug discovery and protein engineering. [ 2 ]  Furthermore, every protein that is added to the structural database increases the likelihood that the database will include homologous sequences of other unknown proteins. The  Protein Structure Initiative  (PSI) is a multifaceted effort funded by the  National Institutes of Health  with various academic and industrial partners that aims to increase knowledge of protein structure using a structural genomics approach and to improve structure-determination methodology.


Structural genomics takes advantage of completed genome sequences in several ways in order to determine protein structures.  The gene sequence of the target protein can also be compared to a known sequence and structural information can then be inferred from the known protein's structure. Structural genomics can be used to predict novel protein folds based on other structural data.  Structural genomics can also take modeling-based approach that relies on homology between the unknown protein and a solved protein structure.


Completed genome sequences allow every  open reading frame  (ORF), the part of a gene that is likely to contain the sequence for the  messenger RNA  and protein, to be cloned and expressed as protein. These proteins are then purified and crystallized, and then subjected to one of two types of structure determination:  X-ray crystallography  and  nuclear magnetic resonance  (NMR).  The whole genome sequence allows for the design of every primer required in order to amplify all of the ORFs, clone them into bacteria, and then express them.  By using a whole-genome approach to this traditional method of protein structure determination, all of the proteins encoded by the genome can be expressed at once.  This approach allows for the structural determination of every protein that is encoded by the genome.


This approach uses protein sequence data and the chemical and physical interactions of the encoded amino acids to predict the 3-D structures of proteins with no homology to solved protein structures. One highly successful method for  ab initio  modeling is the  Rosetta  program, which divides the protein into short segments and arranges short polypeptide chain into a low-energy local conformation.  Rosetta is available for commercial use and for non-commercial use through its public program, Robetta.


This modeling technique compares the gene sequence of an unknown protein with sequences of proteins with known structures. Depending on the degree of similarity between the sequences, the structure of the known protein can be used as a model for solving the structure of the unknown protein. Highly accurate modeling is considered to require at least 50% amino acid sequence identity between the unknown protein and the solved structure. 30-50% sequence identity gives a model of intermediate-accuracy, and sequence identity below 30% gives low-accuracy models.  It has been predicted that at least 16,000 protein structures will need to be determined in order for all structural motifs to be represented at least once and thus allowing the structure of any unknown protein to be solved accurately through modeling. [ 3 ]   One disadvantage of this method, however, is that structure is more conserved than sequence and thus 	sequence-based modeling may not be the most accurate way to predict protein structures.


Threading  bases structural modeling on fold similarities rather than sequence identity.  This method may help identify distantly related proteins and can be used to infer molecular functions.


There are currently a number of on-going efforts to solve the structures for every protein in a given proteome.


One current goal of the  Joint Center for Structural Genomics  (JCSG), a part of the  Protein Structure Initiative  (PSI) is to solve the structures for all the proteins in  Thermotoga maritima , a thermophillic bacterium.  T. maritima  was selected as a structural genomics target based on its relatively small genome consisting of 1,877 genes and the hypothesis that the proteins expressed by a thermophilic bacterium would be easier to crystallize.


Lesley  et al  used  Escherichia coli  to express all the open-reading frames (ORFs) of  T. martima . These proteins were then crystallized and structures were determined for successfully crystallized proteins using X-ray crystallography.  Among other structures, this structural genomics approach allowed for the determination of the structure of the TM0449 protein, which was found to exhibit a novel fold as it did not share structural homology with any known protein. [ 4 ]


The goal of the  TB Structural Genomics Consortium  is to determine the structures of potential drug targets in  Mycobacterium tuberculosis , the bacterium that causes tuberculosis. The development of novel drug therapies against tuberculosis are particularly important given the growing problem of  multi-drug-resistant tuberculosis .


The fully sequenced genome of  M. tuberculosis  has allowed scientists to clone many of these protein targets into expression vectors for purification and structure determination by X-ray crystallography.  Studies have identified a number of target proteins for structure determination, including extracellular proteins that may be involved in pathogenesis, iron-regulatory proteins, current drug targets, and proteins predicted to have novel folds. So far, structures have been determined for 708 of the proteins encoded by  M. tuberculosis .


Subfunctionalization  was proposed by Stoltzfus (1999) [ 1 ]  and Force et al. (1999) [ 2 ]  as one of the possible outcomes of  functional divergence  that occurs after a  gene duplication  event, in which pairs of genes that originate from duplication, or  paralogs , take on separate functions. [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ]  Subfunctionalization is a neutral mutation process of  constructive neutral evolution ; meaning that no new adaptations are formed. [ 8 ] [ 7 ]  During the process of gene duplication paralogs simply undergo a division of labor by retaining different parts (subfunctions) of their original ancestral function. [ 9 ]  This partitioning event occurs because of segmental gene silencing leading to the formation of paralogs that are no longer duplicates, because each gene only retains a single function. [ 7 ]   It is important to note that the ancestral gene was capable of performing both functions and the descendant duplicate genes can now only perform one of the original ancestral functions. [ 7 ]


Subfunctionalization after  gene duplication  is thought to be the newer model of  functional divergence . [ 10 ]  Before 1910, scientists were unaware that genes were capable of multifunctionalization. [ 7 ]  The original thought was that each gene possessed one function, but in fact genes have independently mutable regions and possessed the ability to subfunctionalize. [ 11 ] [ 7 ]   Neofunctionalization , where one paralogous copy derives a new function after gene duplication, is thought to be the classical model of functional divergence. [ 11 ]   Nevertheless, because of its  neutral mutation  process subfunctionalization seem to present a more  parsimonious  explanation for the retention of duplicates in a genome. [ 6 ] [ 12 ] [ 13 ]


Specialization is a unique model of subfunctionalization, in which paralogs divide into various areas of specialty rather than function. In this model both gene copies perform exactly the same ancestral function.  For instance, while the ancestral gene may have performed its function in all tissues, developmental stage, and environmental conditions, the paralogous genes become specialists, dividing themselves among different tissues, developmental stages, and environmental conditions. [ 14 ]   For example, if the ancestral gene is responsible for both digestive and lymphatic regulatory processes, after gene duplication one of the paralogs would claim responsibility for lymphatic regulation and the other for digestive regulation.
Specialization is also unique in the fact that it is a positive rather than neutral mutation process. [ 7 ]   When a gene specializes among different tissues, developmental stages, or environmental conditions it acquires an improvement in function.  Isozymes  are a good example of this because they are gene products of paralogs that catalyze the same biochemical reaction. [ 14 ]  However, different members have evolved particular adaptations to different tissues or different developmental stages that enhance the physiological fine-tuning of the cell. [ 7 ]


Gene sharing occurs when a gene acquires a secondary function during its evolutionary process. Gene sharing is unique because the gene maintains and performs both its ancestral function and its acquired function. Gene duplication is not necessary in this model, as the addition of functionality occurs before, or often instead of gene duplication.
Gene sharing is a fairly common occurrence and is most often seen in enzymes taking on a various subfunctions such as signal transduction and transcriptional regulation. [ 7 ]   The most noteworthy example of gene sharing is crystallins, the proteins responsible for transparency and diffraction in the eye lens, which have also been found to serve as a metabolic enzyme in other tissue. [ 7 ]


Adaptive conflict arises in gene sharing when an improvement to one gene function severely impairs another function.  This occurs because selective constraints are particularly stringent in the case of gene sharing. [ 7 ]  It is very difficult for either function to undergo morphological changes, due to the fact that both the ancestral and novel functions are needed. As a result of its dual function the gene is subjected to two or more independent sets of evolutionary pressure. [ 7 ]  This means that positively selecting for improvements in one function is likely to cause deleterious effect in the other function. There are two solutions to the predicament of adaptive conflict. The gene can either completely lose its new function or undergo gene duplication followed by subfunctionalization, [ 7 ]  also called "function splitting". [ 15 ]


In the Duplication- Degeneration- Complementation (DDC) model of subfunctionalization both gene copies are needed to perform the original ancestral function. [ 10 ]  In this model after a duplication event, both paralogs suffer deleterious mutations leading to functional degradation. This degradation is so severe that neither gene copy can perform the ancestral function or any subset of that function independently.  In order to be functional, the paralogs must work together to perform the ancestral task. This teamwork among paralogs is possible because the subfunction lost in one gene copy is complemented in the other gene copy. [ 7 ]  This functional sharing would not be possible if both paralogs had lost identical subfunctions.
The degeneration and complementation processes make the DDC model a selectively neutral mutation process. The mutations accumulated in both paralogs would have been deleterious if they had not been complemented by the other copy. [ 7 ]  One example of the DDC model is when functionally similar paralogs are expressed at such low levels that both copies are required to produce sufficient amounts of the original gene product. [ 7 ]


Segregation avoidance occurs when an unequal crossing over event leads to a locus duplication containing two heterogeneous alleles creating a situation akin to permanent heterozygosity. [ 7 ]  This occurs primarily in situations of overdominant selection where the heterozygote has increased fitness but less fit homozygotes are still retained in the population. [ 7 ]
Segregation avoidance addresses the issue of segregational load, wherein the mean fitness of the population is less than the highest possible fitness.  The unequal crossing over and subsequent duplication of a locus containing heterogeneous alleles ensures the highest possible fitness.  By avoiding homogeneous alleles organisms in the population can benefit from the advantages that both alleles have to offer.  A prime example is the ace-1 locus in house mosquitoes,  Culex pipiens . [ 16 ]   Because of segregation avoidance house mosquitos are able to benefit from ace-1R pesticide resistant allele during pesticide exposure and ace-1S wild-type allele during non-exposure. [ 7 ]   This duality is particularly useful, as the mutant allele causes decreased fitness during period's non-exposure. [ 16 ]


Human  hemoglobin  provides a variety of subfunctionalization examples.  For instance, the gene for hemoglobin α-chain is undoubtedly derived from a duplicate copy of hemoglobin β-chain. [ 7 ]  However, neither chain can function independently to form a monomeric hemoglobin molecule, that is a molecule consisting entirely of α-chains or entirely of β-chains. [ 7 ]   Conversely, hemoglobin consists of both α and β chains, with α2-β2 being among the most efficient forms of hemoglobin in the human genome. This is a prime example of subfunctionalization.
Another good example is the emergence of fetal hemoglobin from embryonic hemoglobin after duplication of the hemoglobin γ- chain. [ 7 ]  This example of subfunctionalization illustrates how different forms of hemoglobin are present at various developmental stages. In fact, there is distinct hemoglobin at each developmental stage: ζ2-ε2 and α2-ε2 in the embryo, α2-γ2 in the fetus, and α2-β2 and α2-δ2 in adults. [ 7 ]   Each type of hemoglobin has advantages that are particular to the developmental stage in which it thrives.  For example, embryonic and fetal hemoglobin have higher oxygen affinity than adult hemoglobin giving them improved functionality in hypoxic environments such as the uterus. [ 7 ]


A  tag SNP  is a representative  single nucleotide polymorphism (SNP)  in a region of the genome with high  linkage disequilibrium  that represents a group of SNPs called a  haplotype . It is possible to identify genetic variation and association to phenotypes without genotyping every SNP in a chromosomal region. This reduces the expense and time of mapping genome areas associated with disease, since it eliminates the need to study every individual SNP. Tag SNPs are useful in  whole-genome SNP association studies  in which hundreds of thousands of SNPs across the entire genome are genotyped.


Two  loci  are said to be in  linkage equilibrium (LE)  if their inheritance is an independent event. If the alleles at those loci are non-randomly inherited then we say that they are at  linkage disequilibrium (LD) . LD is most commonly caused by physical linkage of genes. When two genes are inherited on the same chromosome, depending on their distance and the likelihood of recombination between the loci they can be at high LD. However, LD can be also observed due to functional interactions where even genes from different chromosomes can jointly confer an evolutionarily selected phenotype or can affect the viability of potential offspring.


In families LD is highest because of the lowest numbers of recombination events (fewest meiosis events). This is especially true between inbred lines. In populations LD exists because of selection, physical closeness of the genes that causes low recombination rates or due to recent crossing or migration. On a population level, processes that influence linkage disequilibrium include  genetic linkage ,  epistatic natural selection ,  rate of recombination ,  mutation ,  genetic drift ,  random mating ,  genetic hitchhiking  and  gene flow . [ 2 ]


When a group of SNPs are inherited together because of high LD there tends to be redundant information. The selection of a tag SNP as a representative of these groups reduces the amount of redundancy when analyzing parts of the genome associated with traits/diseases. [ 3 ]  The regions of the genome in high LD that harbor a specific set of SNPs that are inherited together are also known as  haplotypes . Therefore, tag SNPs are representative of all SNPs within a haplotype.


The selection of tag SNPs is dependent on the haplotypes present in the genome. Most sequencing technologies provide the genotypic information and not the haplotypes i.e. they provide information on the specific bases that are present but do not provide phasic information (at which specific chromosome each of the bases appear). [ 4 ]  Determination of haplotypes can be done through molecular methods ( Allele Specific PCR ,  Somatic cell hybrids ). These methods distinguish which allele is present at which chromosome by separating the chromosomes before genotyping. They can be very time-consuming and expensive, so statistical inference methods have been developed as a less expensive and automated option. These statistical-inference software packages utilize parsimony, maximum likelihood, and Bayesian algorithms to determine haplotypes. Disadvantage of statistical-inference is that a proportion of the inferred haplotypes could be wrong. [ 5 ]


When haplotypes are used for genome wide association studies, it is important to note the population being studied. Often different populations will have different patterns of LD. One example of differentiating patterns are African-descended populations vs. European and Asian-descended populations. Since humans originated in Africa and spread into Europe and then the Asian and American continents, the African populations are the most genetically diverse and have smaller regions of LD while European and Asian-descended populations have larger regions of LD due to  founder effect . When LD patterns differ in populations, SNPs can become disassociated with each other due to the changes in  haplotype blocks . This means that tag SNPs, as representatives of the haplotype blocks, are unique in populations and population differences should be taken into account when performing association studies. [ 6 ]


Almost every trait has both genetic and environmental influence.  Heritability  is the proportion of phenotypic variance that is inherited from our ancestors. Association studies are used to determine the  genetic influence on phenotypic presentation . Although mostly used for mapping diseases to genomic areas, they can also be used to map heritability of any phenotype like height, eye color etc.


Genome-wide association studies (GWAS)  use  single-nucleotide polymorphisms (SNPs)  to identify genetic associations with clinical conditions and phenotypic traits. [ 8 ]  They are hypothesis free and use a whole-genome approach to investigate traits by comparing a large group of individuals that express a phenotype with a large group of people that don't. The ultimate goal of GWAS is to determine genetic risk factors that can be used to make predictions about who is at risk for a disease, what are the biological underpinnings of disease susceptibility and creating new prevention and treatment strategies. [ 1 ]  The  National Human Genome Research Institute  and the  European Bioinformatics Institute  publishes the  GWAS Catalog , a catalog of published genome-wide association studies that highlights statistically significant associations between hundreds of SNPs with a broad range of phenotypes. [ 9 ]


Due to the large number of possible SNP variants (more than 149 million as of June 2015 [ 10 ] [ 11 ] ) it is still very expensive to sequence all SNPs. That is why GWAS use customizable arrays  (SNP chips)  to genotype only a subset of the variants identified as tag snps. Most GWAS use products from the two primary genotyping platforms. The  Affymetrix  platform prints DNA probes on a glass or silicone chip that hybridize to specific alleles in the sample DNA. The  Illumina  platform uses bead-based technology, with longer DNA sequences and produces better specificity. [ 1 ]  Both platforms are able to genotype more than a million tag SNPs using either pre-made or custom  DNA oligos .


Genome-wide studies are predicated on the  common disease-common variant (CD/CV) hypothesis  which states that common disorders are influenced by common genetic variation. Effect size ( penetrance ) of the common variants needs to be smaller relative to those found in rare disorders. That means that the common SNP can explain only a small portion of the variance due to genetic factors and that common diseases are influenced by multiple common alleles of small effect size. Another hypothesis is that common diseases are caused by rare variants that are  synthetically linked  to common variants. In that case the signal produced from GWAS is an indirect (synthetic) association between one or more rare causal variants in linkage disequilibrium. It is important to recognize that this phenomenon is possible when selecting a group for tag SNPs. When a disease is found to be associated with a haplotype, some SNPs in that haplotype will have synthetic association with the disease. To pinpoint the causal SNPs we need a greater resolution in the selection of haplotype blocks. Since whole genome sequencing technologies are rapidly changing and becoming less expensive it is likely that they will replace the current genotyping technologies providing the resolution needed to pinpoint causal variants.


Because whole genome sequencing of individuals is still cost prohibitive, the  international HapMap Project  was constructed with a goal to map the human genome to haplotype groupings (haplotype blocks) that can describe common patterns of human genetic variation. By mapping the entire genome to haplotypes, tag SNPs can be identified to represent the haplotype blocks examined by genetic studies. An important factor to consider when planning a genetic study is the frequency and risk incurred by specific alleles. These factors can vary in different populations so the HapMap project used a variety of sequencing techniques to discover and catalog SNPs from different sets of populations. Initially the project sequenced individuals from Yoruba population of African origin (YRI), residents of Utah with western European ancestry (CEU), unrelated individuals from Tokyo, Japan (JPT) and unrelated Han Chinese individuals from Beijing, China (CHB).  Recently their datasets have been expanded to include other populations (11 groups). [ 1 ]


Selection of maximum informative tag SNPs is an  NP complete problem . However, algorithms can be devised to provide approximate solution within a margin of error. [ 12 ]  The criteria that are needed to define each tag SNP selection algorithm is the following:


Methods for selecting features fall into two categories: filter methods and wrapper methods. Filter algorithms are general preprocessing algorithms that do not assume the use of a specific classification method. Wrapper algorithms, in contrast, “wrap” the feature selection around a specific classifier and select a subset of features based on the classifier's accuracy using cross-validation. [ 13 ]


The feature selection method suitable for selecting tag SNPs must have the following characteristics:


Several algorithms have been proposed for selecting tag SNPs. The first approach was based on the measure of goodness of SNP sets and searched for SNP subsets that are small but attain high value of the defined measure. Examining every SNP subset to find good ones is computationally feasible only for small data sets.


Another approach uses  principal component analysis (PCA)  to find subsets of SNPs capturing majority of the data variance. A sliding windows method is employed to repeatedly apply PCA to short chromosomal regions.  This reduces the data produced and also does not require exponential search time. Yet it is not feasible to apply the PCA method to large chromosomal data sets as it is computationally complex. [ 13 ]


The most commonly used approach, block-based method, exploits the principle of linkage disequilibrium observed within haplotype blocks. [ 12 ]  Several algorithms have been devised to partition chromosomal regions into haplotype blocks which are based on  haplotype diversity ,  LD ,  four-gamete test  and  information complexity  and tag SNPs are selected from all SNPs that belong to that block. The main presumption in this algorithm is that the SNPs are  biallelic . [ 14 ]  The main drawback is that the definition of blocks is not always straightforward. Even though there is a list of criteria for forming the haplotype blocks, there is no consensus on the same. Also, local correlations based selection of tag SNPs ignores inter-block correlations. [ 12 ]


Unlike the block-based approach, a block-free approach does not rely on the block structure. The SNP frequency and recombination rates are known to vary across the genome and some studies have reported LD distances much longer than the reported maximum block sizes. Setting a strict border for the neighborhood is not desired and the block-free approach looks for tag SNPs globally. There are several algorithms to perform this. In one algorithm, the non-tagging SNPs are represented as boolean functions of tag SNPs and  set theory  techniques are used to reduce search space. Another algorithm searches for subsets of markers that can come from non-consecutive blocks. Due to the marker neighborhood, the search space is reduced. [ 13 ]


With the number of individuals genotyped and number of SNPs in databases growing, tag SNP selection takes too much time to compute. In order to improve the efficiency of the tag SNP selection method, the algorithm first ignores the SNPs being biallelic, and then compresses the length (SNP number) of the haplotype matrix by grouping the SNP sites with the same information. The SNP sites that partition the haplotypes into the same group are called redundant sites. The SNP sites which contain distinct information within a block are called non-redundant sites (NRS). In order to further compress the haplotype matrix, the algorithm needs to find the tag SNPs such that all haplotypes of the matrix can be distinguished. By using the idea of joint partition, an efficient tag SNPs selection algorithm is provided. [ 14 ]


Depending on how the tag SNPs are selected, different prediction methods have been used during the cross-validation process. Machine learning method was employed to predict the left-out haplotype. Another approach predicted the alleles of a non-tagging SNP n from the tag SNPs that had the highestcorrelation coefficient with n. If a single highly correlated tag SNP t is found, the alleles are assigned so their frequencies agree with the allele frequencies of t. When multiple tagging SNPs have the same (high) correlation coefficient with n, the common allele of n has advantage. It is easy to see that in this case the prediction method agrees well with the selection method, which uses PCA on the matrix of correlation coefficients between SNPs. [ 13 ]


There are other ways to assess the accuracy of a tag SNP selection method. The accuracy can be evaluated by the quality measure R2, which is the measure of association between the true numbers of haplotype copies defined over the full set of SNPs and the predicted number of haplotype copies where the prediction is based on the subset of tagging SNPs. This measure assumes diploid data and explicit inference of haplotypes from genotypes. [ 13 ]


Another assessment method due to Clayton is based on a measure of the diversity of haplotypes. The diversity is defined as the total number of differences in all pairwise comparison between haplotypes. The difference between a pair of haplotypes is the sum of differences over all the SNPs. The Clayton's diversity measure can be used to define how well a set of tag SNPs differentiate different haplotypes. This measure is suitable only for haplotype blocks with limited haplotype diversity and it is not clear how to use it for large data sets consisting of multiple haplotype blocks. [ 13 ]


Some recent works evaluate tag SNPs selection algorithms based on how well the tagging SNPs can be used to predict non-tagging SNPs. The prediction accuracy is determined using cross-validation such as leave-one-out or hold out. In leave-one-out cross-validation, for each sequence in the data set, the algorithm is run on the rest of the data set to select a minimum set of tagging SNPs. [ 13 ]


Tagger is a web tool available for evaluating and selecting tag SNPs from genotypic data such as the International HapMap Project. It utilizes pairwise methods and multimarker haplotype approaches. Users can upload HapMap genotype data or pedigree format and the linkage disequilibrium patterns will be calculated. Tagger options allow for the user to specify chromosomal landmarks, which indicate regions of interest in the genome for picking tag SNPs. The program then produces a list of tag SNPs and their statistical test values as well as a coverage report.  It is developed by Paul de Bakker in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of  Massachusetts General Hospital  and  Harvard Medical School , at the  Broad Institute . [ 15 ]


In the freeware CLUSTAG and WCLUSTAG, there contain cluster and set-cover algorithms to obtain a set of tag SNPs that can represent all the known SNPs in a chromosomal region. The programs are implemented with Java, and they can run in Windows platform as well as the Unix environment.  They are developed by  SIO-IONG AO  et al. in The University of Hong Kong. [ 16 ] [ 17 ]


Takifugu rubripes , commonly known as the  Japanese puffer ,  Japanese pufferfish ,  Tiger puffer , or  torafugu  ( Japanese :  虎河豚 ), is a  pufferfish  in the genus  Takifugu . It is distinguished by a very small  genome  that has been fully sequenced because of its use as a model species and is in widespread use as a reference in genomics. [ 4 ]


The species is often referred to in the genomics literature as  Fugu rubripes . The genus  Fugu  is a synonym of the currently preferred  Takifugu . [ 5 ]   Takifugu  is Japanese for puffer and  rubripes  comes from the Latin  ruber  and  pēs  meaning ruddy foot. [ citation needed ]


The species is known from the  Sea of Japan ,  East China Sea  and  Yellow Sea  north to southern  Sakhalin , at depths of 10–135 m (33–443 ft). It is a  demersal  species. Spawning occurs in  estuaries ; young fish can tolerate a wide range of salinities and will remain in river mouths and lagoons, maturing for one year before migrating permanently to the open ocean. [ 1 ]


A feature of this species is that it has a very small  genome , which is used as a 'reference' for identifying genes and other elements in human and other vertebrate genomes. The genome was published in 2002 by the International Fugu Genome Consortium via  whole genome shotgun sequencing.  After being initiated in 1989, it was the first vertebrate genome after the  human genome  to be made publicly available. [ 6 ]  It remains among the smallest known vertebrate genomes; its number of base pairs is ~6% and the number of previously known protein-coding genes ~13% that of the human genome, although the number of  chromosomes  (22) is comparable to that of humans (23). This makes it particularly useful for comparative studies. Current estimates show a total of 392,376,244  base pairs , 1,138 known and 18,093 novel  protein-coding  genes, and 593  RNA genes . [ 7 ]


One type of torafugu, 22-seiki fugu (meaning "22nd-century fugu"), has been genetically modified by removing four  leptin receptor  genes that control appetite. The result has increased appetite and weight gain, growing on average 1.9 times faster than normal torafugu. It is sold commercially as food. [ 8 ] [ 9 ]


As some other pufferfish, some organs of the Japanese puffer contain  tetrodotoxin  and are highly toxic. The toxin is highly concentrated in liver and ovaries, slightly present in the intestines and flesh, and absent from skin and testes. [ 1 ]


The Japanese puffer is classified as  Near Threatened  by the  IUCN . It is one of the most valuable commercial fishes in Japan, and although current catches (101 tonnes / year in 2004, down from a peak of 2,000 tonnes in 1987) are small compared to those of many other commercial species such as  sardines  and  anchovy , they appear to be unsustainable and prevent the recovery of the species from earlier over-exploitation. Gear restrictions (most catches occur by  longline fishing ) and adjustments of fishery seasons to protect juveniles have been recommended to aid recovery. The species is extensively raised in  aquaculture . [ 1 ]


The  thanatotranscriptome  denotes all  RNA  transcripts produced from the portions of the  genome  still active or awakened in the internal organs of a body following its  death . It is relevant to the study of the  biochemistry ,  microbiology , and  biophysics  of  thanatology , in particular within  forensic science . Some genes may continue to be expressed in  cells  for up to 48 hours after death, producing new  mRNA . Certain genes that are generally inhibited since the end of fetal development may be expressed again at this time. [ 1 ] [ 2 ] [ 3 ]


Clues to the existence of a post-mortem transcriptome existed at least since the beginning of the 21st century, [ 4 ]   but the word  thanatotranscriptome  (from ( thanatos-,  Greek for "death") seems to have been first used in the  scientific literature  by Javan et al. in 2015, [ 2 ]  following the introduction of the concept of the  human thanatomicrobiome  in 2014 at the 66th Annual Meeting of the American Academy of Forensic Sciences in Seattle, Washington. [ 5 ]


In 2016, researchers at the  University of Washington  confirmed that up to 2 days (48 hours) after the death of mice and zebrafish, many genes still functioned. [ 1 ] [ 3 ]  Changes in the quantities of  mRNA  in the bodies of the dead animals proved that hundreds of genes with very different functions awoke just after death. The researchers detected 548 genes that awoke after death in  zebrafish  and 515 in  laboratory mice . Among these were genes involved in  development  of the organism, including genes that are normally activated only  in utero  or  in ovo  (in the egg) during fetal development.


The thanatomicrobiome is characterized by a diverse assortment of microorganisms located in internal organs (brain, heart, liver, and spleen) and blood samples collected after a human dies. It is defined as the microbial community of internal body sites, created by a successional process whereby trillions of microorganisms populate, proliferate, and/or die within the dead body, resulting in temporal modifications in the community composition over time.


Characterization and quantification of the transcriptome in a given "dead" tissue can identify  genetic  assets, which can be used to determine the regulatory mechanisms and set networks of  gene expression .


The techniques commonly used for simultaneously measuring the concentration of a large number of different types of mRNA include  microarrays  and high-throughput sequencing via  RNA-Seq .


Analysis from a  serology  postmortem can characterize the  transcriptome  of a particular  tissue  cell type, or compare the transcriptomes between various experimental conditions. Such analysis can be complementary to the analysis of thanatomicrobiome to better understand the process of transformation of the necromass in the hours and days following death. [ 6 ]


Future applications of this information could include:


Toxgnostics  is part of  personalized medicine  as it describes the guiding principles for the discovery of  pharmacogenomic  biomarker tests, also referred to as  companion diagnostic  tests, which identify if an individual patient is likely to suffer severe drug toxicity from treatment with a specific therapeutic agent. Once at-risk individuals are identified, drug toxicity can be prevented using elective dose reduction or prescription of a different medication. [ 1 ] [ 2 ] [ 3 ]


The majority of toxgnostic studies have been candidate gene studies restricted to the known Absorption, Distribution, Metabolism, and Excretion genes ( ADME ) of drug treated patients.  The PharmaADME consortium [ 4 ]  identified 32 core genes containing 184 variants within common pathways that should be included in ADME candidate gene studies of toxicity biomarkers. Toxicity biomarkers that have been clinically validated using this restricted panel of genes include the  P450 cytochrome  assay that is currently recommended for routine clinical use of the oral anticoagulant warfarin. Using  next-generation sequencing  methods and  genome-wide association studies  a more comprehensive toxgnostic approach can be utilized through unbiased analysis of several million variants across the whole human genome, including introns and exons, for pharmacogenomic markers of drug induced toxicity. [ 1 ]


Cancer drugs have been highlighted as particularly appropriate candidates for toxgnostic studies due to the significant toxicity profiles associated with both targeted therapies [ 5 ]  and chemotherapy. [ 6 ]  Most cancer patients obtain only modest benefit from treatment, whereas toxicity is common and often associated with severe side effects which include considerable morbidity and mortality. One of the most commonly used chemotherapy drugs  5-fluorouracil  (5FU) prescribed as  adjuvant therapy  following surgical resection of early stage  colorectal cancer  benefits only approx. 4% of patients, whereas 30–40% of those treated will suffer severe toxicity such as  neutropenia ,  mucositis ,  hand-foot syndrome ,  diarrhoea , and  stomatitis , fatal toxicities will kill 0.5-1% of people treated. [ 7 ]  Through the use of toxgnostic screens a number of genetic variants have now been identified that can be used to predict 5FU toxicity prior to treatment. [ 8 ]  These genetic variants can be used to identify the individuals predisposed to severe drug toxicity and the dose of 5FU chemotherapy can be reduced to prevent severe toxic side effects. Toxgnostic biomarker tests currently available for use in clinical practice include markers for  irinotecan ,  thioguanine ,  warfarin  and  5FU .


Toxgnostic studies are defined by four key elements: [ 1 ]


Appropriate analytical approaches for toxgnostic studies include  candidate gene  studies,  GWAS  and  whole genome sequencing . GWAS and whole genome sequencing are the most comprehensive approaches though careful considerations must be applied to the relevance, analysis and interpretation of the results to prevent over-fitting, which produces false-positive results, a proposed GWAS workflow is shown below. [ 1 ]


Toxicity biomarkers can be co-developed and co-approved with the respective drug as a  companion diagnostic test , this requires  premarket approval  (PMA). The  Food and Drug Administration  (FDA) IVD Companion Diagnostic Device guidance issued in draft 14 July 2011 states that a companion diagnostic test can be used to “Identify patients likely to be at increased risk for serious adverse reactions as a result of treatment with a particular therapeutic product”. [ 9 ]  Additionally there are guidelines for the submission of pharmacogenomic studies from the FDA [ 10 ]  and draft guidance from the  European Medicines Agency  (EMA). [ 11 ]


Toxicogenomics  is a subdiscipline of  pharmacology  that deals with the collection, interpretation, and storage of information about  gene  and  protein  activity within a particular  cell  or  tissue  of an  organism  in response to exposure to  toxic substances . Toxicogenomics combines  toxicology  with  genomics  or other high-throughput molecular profiling technologies such as  transcriptomics ,  proteomics  and  metabolomics . [ 1 ] [ 2 ]  Toxicogenomics endeavors to elucidate the molecular mechanisms evolved in the expression of toxicity, and to derive molecular expression patterns (i.e., molecular  biomarkers ) that predict toxicity or the genetic susceptibility to it. [ 3 ]


In pharmaceutical research, toxicogenomics is defined as the study of the structure and function of the  genome  as it responds to adverse xenobiotic exposure. It is the toxicological subdiscipline of  pharmacogenomics , which is broadly defined as the study of inter-individual variations in whole-genome or candidate gene  single-nucleotide polymorphism  maps,  haplotype  markers, and alterations in  gene expression  that might correlate with drug responses. [ 4 ] [ 5 ]  Though the term toxicogenomics first appeared in the literature in 1999, [ 6 ]  it was by that time already in common use within the pharmaceutical industry as its origin was driven by marketing strategies from vendor companies. The term is still not universally accepted, and others have offered alternative terms such as  chemogenomics  to describe essentially the same field of study. [ 7 ]


The nature and complexity of the data (in volume and variability) demands highly developed processes of automated handling and storage. The analysis usually involves a wide array of  bioinformatics  and  statistics , [ 8 ]  often including  statistical classification  approaches. [ 9 ]


In pharmaceutical  drug discovery  and  development , toxicogenomics is used to study possible  adverse  (i.e.  toxic ) effects of pharmaceutical  drugs  in defined  model systems  in order to draw conclusions on the toxic risk to patients or the environment. Both the  United States Environmental Protection Agency  (EPA) and the  Food and Drug Administration  (FDA) currently preclude basing regulatory decision-making on genomics data alone. However, they do encourage the voluntary submission of well-documented, quality genomics data. Both agencies are considering the use of submitted data on a case-by-case basis for assessment purposes (e.g., to help elucidate mechanism of action or contribute to a weight-of-evidence approach) or for populating relevant comparative databases by encouraging parallel submissions of genomics data and traditional toxicological test results. [ 10 ]


Chemical Effects in Biological Systems is a project  hosted by the  National Institute of Environmental Health Sciences  building a knowledge base of toxicology studies including study design, clinical pathology, and  histopathology  and toxicogenomics data. [ 11 ] [ 12 ]


InnoMed PredTox assesses the value of combining results from various  omics  technologies together with the results from more conventional toxicology methods in more informed decision-making in preclinical safety evaluation. [ 13 ]


Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) is a Japanese public-private effort which has published gene expression and pathology information for more than 170 compounds (mostly drugs). [ 14 ]


The Predictive Safety Testing Consortium aims to identify and clinically qualify safety biomarkers for regulatory use as part of the  FDA 's "Critical Path Initiative". [ 13 ]


ToxCast is a program for Predicting Hazard, Characterizing Toxicity Pathways, and Prioritizing the Toxicity Testing of Environmental Chemicals at the  United States Environmental Protection Agency . [ 15 ]


Tox21 is a federal collaboration involving the  National Institutes of Health  (NIH), Environmental Protection Agency (EPA), and Food and Drug Administration (FDA), is aimed at developing better toxicity assessment methods. [ 16 ]  Within this project the toxic effects of chemical compounds on cell lines derived from the  1000 Genomes Project  individuals were assessed and associations with genetic markers were determined. [ 17 ]  Parts of this data were used in the NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge in order to determine methods for cytotoxicity predictions for individuals. [ 18 ] [ 19 ]


Translatomics  is the study of all  open reading frames  (ORFs) that are being actively translated in a  cell  or  organism . This collection of ORFs is called the  translatome . Characterizing a cell's translatome can give insight into the array of  biological pathways  that are active in the cell. According to the  central dogma of molecular biology , the  DNA  in a cell is  transcribed  to produce  RNA , which is then translated to produce a  protein . Thousands of proteins are encoded in an organism's  genome , and the proteins present in a cell cooperatively carry out many functions to support the life of the cell. Under various conditions, such as during stress or specific timepoints in development, the cell may require different biological pathways to be active, and therefore require a different collection of proteins. Depending on intrinsic and environmental conditions, the collection of proteins being made at one time varies. Translatomic techniques can be used to take a "snapshot" of this collection of actively translating ORFs, which can give information about which biological pathways the cell is activating under the present conditions. [ 1 ]


Usually, the  ribosome profiling  technique is used to acquire the translatome information. [ 2 ]  Recent advancements, including single-cell ribosome profiling, have significantly improved the resolution of these studies, allowing researchers to gain insights into translation at the level of individual cells. [ 3 ]  This is particularly important for heterogeneous cell populations, where overall bulk measurements may mask important cell-to-cell differences in protein synthesis. Other methods are  polysome profiling , full-length translating mRNA profiling ( RNC-seq ), and translating ribosome affinity purification (TRAP-seq). [ 4 ]  Unlike the  transcriptome , the translatome is a more accurate approximation for estimating the expression level of some  genes , since the correlation between the  proteome  and translatome is higher than the correlation between the transcriptome and proteome. [ 5 ]


Nearing the completion of the  Human Genome Project  the field of genetics was shifting its focus toward determining the functions of genes. This involved cataloguing other collections of biological materials, like RNA and proteins in cells. These collections of materials were called -omes, evoking the widespread excitement surrounding the sequencing of the human genome. The term translatome was first proposed in 2001 by Greenbaum et al. [ 6 ]  The translatome was intended to describe the relative quantities of proteins in a  proteome . The term translatome now generally refers to the collection of proteins actively being created in a cell. Translatomics, in combination with  degradomics , aims to describe the net change to the proteome under different conditions.


The aim of  genomics  is to study the genome, or the collection of genetic material in an organism. Genomics subfields, or other - omics , such as  Transcriptomics  and  proteomics  aim to characterize genome function by quantifying products of the genome (such as RNA and proteins) under different conditions. In doing so, omics gain insight into different levels of regulation of  gene expression  and therefore genome function. However, these fields characterize  biomolecules  that have already been formed. In some cases, RNA or protein abundance does not reflect function because these biomolecules may be degraded rapidly, or they may remain in a cell long after they are initially synthesized. When using proteomics techniques to study the  proteome , regulation of protein abundance at the level of  post-translational modification  and protein degradation may obscure earlier regulatory processes. Because cellular functions are often regulated at the level of translation, meaning the  transcriptome  does not always reflect genome function, [ 1 ]  using translatomics techniques to study the translatome may allow one to observe regulation of genome function that would be obscured in transcriptomics or proteomics studies.


Most translatomics techniques focus on characterizing the mRNAs that are complexed with ribosomes and therefore being translated.


Polysome profiling  is a technique used to characterize the degree of translation of one or more mRNAs. A highly translated mRNA exists as a  polysome , meaning it is complexed with multiple ribosomes. mRNAs translated at lower levels are complexed with fewer ribosomes. In polysome profiling, a sucrose gradient is used to separate molecular complexes in a cell lysate based on size. [ 7 ]  The fractions from the column are analyzed by sequencing or other methods. The translation rate of mRNAs is determined based on its detection and abundance in the fractions of lower and higher molecular weight.


The full length translating mRNA (RNC-seq) involves centrifugation of lysated sample on a sucrose cushion. This allows separation of the Ribosome-nascent chain complex(RNC) from free mRNAs and other cell components. The RNCs form a pellet in the centrifugation that is collected for further analysis. The mRNA being translated in these RNCs can be sequenced, allowing identification and quantification of the mRNAs being translated at the time. [ 1 ] [ 8 ]  However, RNC-mRNA complexes are fragile which can lead to ribosomes to dissociate from the mRNAs and degradation of mRNAs, potentially biasing the collected results. [ 1 ]


In  Ribosome profiling , cellular mRNA including polysomes is subjected to ribonucleases, enzymes that cleave RNA. Those positions in the RNA molecules that are bound by ribosomes are protected against digestion. After cessation of ribonuclease activity, these protected sites can be recovered and sequenced. The sequences obtained from ribo-seq therefore represent fragments of mRNAs that were being actively translated. [ 9 ]


TRAP-seq is used to identify mRNAs being actively translated in a specific cell type within a tissue or other assortment of cells. The cell type of interest is engineered to express a ribosomal subunit fused to an epitope tag such as  green fluorescent protein . [ 10 ]  After cell lysis, antibodies targeting the epitope are used to isolate mRNAs that are bound to the ribosomes containing the fusion proteins. This RNA is then converted to  cDNA  and sequenced. This technique specifically identifies mRNAs that were being translated in the cell type of interest.


Mass spectrometry  methods are unable to determine folding of nascent  polypeptides . No current methods examine the folding states of nascent polypeptides globally in the cell. There are methods for examining the folding state of individual nascent polypeptides. [ 1 ]


One method uses a nonspecific protease to cleave the nascent peptide at a low temperature. The protease can cleave the unfolded, flexible regions, but cannot cut tightly folded regions. The products of the cleavage can then be separated and studied to determine the folded regions of the nascent peptide. [ 1 ] [ 11 ]


To analyze the full structure of a nascent polypeptide,  nuclear magnetic resonance (NMR)  is used. NMR allows a dynamic view of molecules in solution and so can be used on  ribosome-nascent chain complexes (RNC) . Labelling of ribosomes allows the NMR data to be filtered for suspected ribosome signal and identify the signal of the nascent polypeptide. [ 12 ]


While the structure of nascent polypeptides is currently unexaminable on a global scale, identification and quantification is not.


One method uses a variant of  Stable isotope labeling by amino acids in cell culture  (SILAC). SILAC labels proteins with stable isotopes to allow quantification, comparing labelled and unlabeled peptides for quantification. Pulse SILAC (pSILAC), only allows peptides created during the pulse to be labelled. In theory, this allows a capture of only nascent peptides for quantification. SILAC, however, requires similar levels of labelled and unlabelled proteins for accurate quantification. As such, pSILAC pulses have to run much longer than the translation process, making quantification of nascent peptides inaccurate. [ 1 ] [ 13 ]


Bio-Orthogonal/Quantitative Non-Canonical Amino acid Tagging (BONCAT/QuaNCAT) uses azidohomoalanine (AHA) to tag proteins. This allows isolation of newly created proteins for MS. [ 14 ] [ 15 ]  However, using AHA requires predepletion of intracellular methionine and introduction of AHA, stressing the cell and potentially altering translation dynamics within. Similar to pSILAC, AHA methods require longer pulses, thus limiting its efficacy in quantifying nascent peptides. [ 1 ] [ 13 ]


Puromycin-associated nascent chain proteomics (PUNCH-P) uses a puromycin-biotin label to capture nascent polypeptides for MS. Though it does not disturb the cell process, it is less sensitive than other methods in detecting nascent peptides. [ 1 ] [ 13 ]  In addition to these quantification methods, other MS based methods are being worked on.


Degradation of  mRNA  also plays an important part in regulating the translation process.


To explore mechanisms of decay, genome-wide mapping of uncapped and cleaved transcripts (GMUCT), parallel analysis of RNA ends (PARE), and degradome sequencing use the T4 ligase of the Illumina sequencing platform to sequence decapped mRNAs. T4 ligase ligates on RNA with a free 5’ monophosphate. As mature mRNAs have a 5’ cap, they are not bound as substrates, leaving decapped and degrading mRNAs to be bound. [ 16 ]


5′-monophosphorylated ends sequencing (5Pseq) captures both capped and decapped sequences to allow sequencing of both mature mRNA and degraded products. This helps identify mRNA degradation products, and has uses in studying ribosome stalling. [ 1 ] [ 17 ]


These methods study 5’ to 3’ degradation,  miRNA -mediated cleavage, and  nonsense mediated mRNA decay , but cannot measure 3’ to 5’ degradation and other degradation mechanisms.


The techniques above require lysis of cells and thus cannot be performed in living cells. Single molecule fluorescence resonance energy transfer (smFRET) and Nascent chain tracking (NCT) use fluorescence to track translational activity. Both methods track elongation rates of the polypeptides on single mRNAs. [ 18 ] [ 19 ]  Neither technique, however, is capable of high throughput. [ 1 ]


Transfer RNA  (tRNA) is an important part of  Translation . tRNAs read the mRNA, bringing the amino acids the ribosome assembles into a polypeptide. As such, the abundances and types of tRNAs has a large effect on the speed of protein synthesis. [ 20 ]  tRNAs can be very similar to other tRNAs, with some tRNA species only differing by a single nucleotide. This, coupled with similar secondary and tertiary structures makes separating different tRNA species difficult. [ 1 ]


2D-Gel electrophoresis is a classic method used in separating tRNAs. Initially, the tRNAs are denatured in 7M urea and separated in the first gel dimension. 4M urea allows partial refolding for additional separation in the second gel dimension. This method has allowed separation into 48 sets in E. coli and 30 in B. subtilis but has limited resolution. Large numbers of different tRNA species cannot be fully separated by 2D-gel electrophoresis, with only 62 spots found for the 269 rat tRNAs. [ 21 ]


High performance liquid chromatography  can be used to separate tRNAs based on aminoacylated tRNA isoacceptors. This method cannot fully separate the tRNA species and cannot distinguish between codons, though it still can find quantitative differences between different cell lines. [ 21 ]


Mass spectrometry  (MS) can be used to separate tRNAs based on unique endonuclease digestion products. [ 22 ]  This, however, has limited resolution with mixtures of 30 tRNA species and needs fractionation prior to MS in larger groups of tRNA. It also cannot be used to identify deNovo tRNA species as it requires prior knowledge of the digestion patterns of tRNA species. [ 21 ]


Hybridization based microarrays  use the 3’CCA conserved sequence in tRNAs to attach a fluorescent probe. 70-80 nucleotide long probes, covering the length of the tRNA, are then used to bind tRNAs. tRNAs with at least 8 base differences are able to be distinguished with microarrays, but tRNAs with smaller differences bind to the same probe. [ 21 ]


Quantitative reverse transcription,  real time PCR  (qRT-PCR) is another way for identifying and quantifying the tRNAome. A primer for the conserved 3’CCA sequence is used for priming reverse transcription for all tRNA species. Heavy modification of the tRNA nucleotides and the stability of the tRNA molecule can be an issue, so high temperatures and demethylation have been used to counter these. Demethylation has also been used to counteract errors heavy methylation induces in sequencing. [ 1 ]


Ribo-tRNA-seq has been developed to observe the role of tRNA more closely to translation. Similar to Ribo-seq, Ribo-tRNA-seq captures tRNA molecules in ribosomes for library preparation before sequencing [ 23 ]


Transmembrane Protein 217  is a protein encoded by the gene TMEM217. TMEM217 has been found to have expression correlated with the  lymphatic system  and  endothelial  tissues, and has been predicted to have a function linked to the  cytoskeleton . [ 1 ] [ 2 ] [ 3 ]


TMEM217 is located on the  chromosome 6  minus strand at 6p21.2. [ 4 ]  The  gene  consists of 46,857 base pairs and is flanked by TBC1D22B (TBC1 Domain Family Member 22B) and  PIM1 . [ 4 ] [ 5 ]  It was previously known as C6orf128 (Chromosome 6  open reading frame  128). [ 4 ]


TMEM217 has three common  isoforms  formed from the  alternative splicing  of three  exons . Isoform 1 translates for the longest  polypeptide , consisting of 1590 nucleotides. [ 6 ]  The 5’ un-translated region of isoform 1 is relatively short and is predicted to fold into several stem loop domains within conserved areas of the un-translated region. [ 7 ]


The longest polypeptide of transmembrane protein 217 consists of 229 amino acids. [ 8 ]  This protein isoform has a predicted weight of 26.6 kDa and  isoelectric point  at a  pH  of 9.3. [ 9 ] [ 10 ]  It is notably rich in isoleucine and phenylalanine, and deficient in alanine, aspartate, and proline compared to other proteins. [ 11 ]  Transmembrane protein 217 contains the  domain of unknown function , DUF4534, between amino acids 11-171. [ 12 ]


Transmembrane protein 217 is predicted to have four  transmembrane domains . [ 8 ] [ 13 ]  These transmembrane domains consist primarily of uncharged amino acids in predicted  alpha helices . [ 11 ] [ 14 ]  The N-terminus and C-terminus of the protein are predicted to be facing the cytosol with the C-terminus containing a long predicted coiled tail extending from the final transmembrane domain. [ 13 ]


There are several predicted phosphorylation and  glycosylation  sites on transmembrane protein 217 in highly conserved parts of the protein, where the  phosphorylation  sites are located primarily on the C-terminal tail. [ 15 ] [ 16 ] [ 17 ]  There are also two highly conserved  cysteine  residues, which have the potential to form a  disulfide bond  in the extracellular space between the first and second transmembrane domains.


TMEM217 is not ubiquitously expressed. The gene tends to have expression correlated to  lymphatic system , vascular/arterial  endothelial  tissue, and notable expression in the bladder based on expression profiles and  microarray  analysis. [ 1 ] [ 2 ]  Other tissues that have been shown to express TMEM217 include: connective tissues, the liver, mammary glands, the testis, and the cervix. [ 1 ]  Co-expression analyses have found that TMEM217 was up-regulated in response to mechanical stretch in  dermal fibroblast  cells and in response to the  resveratrol  derivative, DMU-212, in vascular  endothelial tissues . [ 3 ] [ 18 ]


No known function has been attributed to TMEM217, however a co-expression analysis in dermal fibroblasts has predicted the protein to have a potential association with the  cytoskeleton . [ 3 ]


Single nucleotide polymorphisms  in TMEM217 have been linked to Alzheimer’s disease and  diabetic retinopathy . [ 19 ] [ 20 ]  TMEM217 was also found to have similar expression patterns as  TRPM2 , a  biomarker  linked to breast carcinoma. [ 21 ]  Expression profiles have also linked elevated TMEM217 expression to bladder cancer and lymphoma. [ 1 ]


TMEM217 was found to have  orthologs  in organisms as early as the scaled fish, which diverged 420 million years ago. [ 22 ]  Although found in organisms as early as fish and reptiles, TMEM217 has no known orthologs in any bird species. [ 23 ] [ 24 ]


TMEM217 has no known  paralogs .


The  TB Structural Genomics Consortium (TBSGC)  is a worldwide consortium of scientists developing a foundation for  tuberculosis  diagnosis and treatment by determining the three-dimensional structures of  proteins  from  M. tuberculosis  founded in 2000 as a part of the  Protein Structure Initiative . [ 1 ] [ 2 ]  The consortium seeks to solve structures of proteins that are of great interest to the TB biology community. [ 3 ]  A major goal of the consortium is to have a putative function for every ORF in the TB  genome .


As of June 2006, 82 TB protein structures have been determined, 15 since January 1, 2006. The database of linked  structural  and  functional  information that has been constructed using this information can form a lasting basis for understanding  M. tuberculosis  pathogenesis and for structure-based drug design. [ citation needed ]


As of June 2006, the TB Structural Genomics Consortium consists of 430 active members in 148 laboratories from 83 institutions across 15 countries. [ 3 ]  Consortium laboratories are collectively responsible for 3.3% of all protein structures in the  protein data bank  and have extensive records of methods development. Consortium members have carried out a pilot project on the structural genomics of a  hyperthermophile  that has identified bottlenecks in the structure determination process and resulted in the development of methodologies for high-throughput structure determination and analysis. The consortium has five core facilities (located at  Lawrence Livermore National Laboratory ,  Los Alamos National Lab ,  Lawrence Berkeley National Laboratory ,  University of California , Los Angeles and  Texas A&M University ) that carry out an increasing fraction of routine tasks such as protein production, crystallization and X-ray data collection. Members of the consortium improve their productivity by sending materials to these facilities, receiving the resulting products or data, and reporting this activity to the database. This helps to minimize redundant pursuits of targets. This structural and functional information is publicly available. [ citation needed ]


The five core facilities available to consortium members provide services for cloning, expression, and purification of proteins as well as crystallization and subsequent diffraction and data analysis of protein crystals. [ 4 ]  Furthermore, a database has been developed to record all activity done within the consortium. This database also tracks the movement of materials between members and allows the up to the minute status to be recorded and available to all other members. [ citation needed ]


UniFrac , a shortened version of  unique fraction metric , is a  distance metric  used for comparing  biological communities . It differs from dissimilarity measures such as  Bray-Curtis dissimilarity  in that it incorporates information on the relative relatedness of community members by incorporating phylogenetic distances between observed organisms in the computation.


Both weighted (quantitative) and unweighted (qualitative) variants of UniFrac [ 1 ]  are widely used in  microbial ecology , where the former accounts for abundance of observed organisms, while the latter only considers their presence or absence. The method was devised by  Catherine Lozupone , when she was working with  Rob Knight [ 2 ]  of the  University of Colorado at Boulder  in 2005. [ 3 ] [ 4 ]


The distance is calculated between pairs of samples (each sample represents an organismal community). All taxa found in one or both samples are placed on a  phylogenetic tree . A branch leading to taxa from both samples is marked as "shared" and branches leading to taxa which appears only in one sample are marked as "unshared". The distance between the two samples is then calculated as:


This definition satisfies the requirements of a  distance metric , being non-negative, zero only when entities are identical, transitive, and conforming to the  triangle inequality .


If there are several different samples, a distance matrix can be created by making a tree for each pair of samples and calculating their UniFrac measure. Subsequently, standard multivariate statistical methods such as  data clustering  and  principal co-ordinates analysis  can be used.


One can determine the statistical significance of the UniFrac distance between two  samples using  Monte Carlo simulations . By randomizing the sample classification of each taxon on the tree (leaving the branch structure unchanged) and creating a distribution of UniFrac distance values, one can obtain a distribution of UniFrac values. From this, a p-value can be given to the actual distance between the samples.


Additionally, there is a weighted version of the UniFrac metric which accounts for the relative abundance of each of the taxa within the communities. This is commonly used in  metagenomic  studies, where the number of metagenomic reads can be in the tens of thousands, and it is appropriate to 'bin' these reads into  operational taxonomic units , or OTUs, which can then be dealt with as taxa within the UniFrac framework.


In 2012, a generalized UniFrac version, [ 5 ]  which unifies the weighted and unweighted UniFrac distance in a single framework, was proposed.  The authors argued that the weighted and unweighted UniFrac distances place too much emphasis on either abundant lineages or rare lineages, respectively, leading to “loss of power when the important composition change occurs in moderately abundant lineages”. The generalized UniFrac distance aims to address this limitation by down-weighting the emphasis on abundant or rare lineages.


Personalized genomics  is the  human genetics -derived study of analyzing and interpreting individualized genetic information by genome sequencing to identify  genetic variations  compared to the  library  of known sequences. International genetics communities have spared no effort from the past and have gradually cooperated to prosecute research projects to determine DNA sequences of the human genome using DNA sequencing techniques. The methods that are the most commonly used are  whole exome sequencing  and  whole genome sequencing . Both approaches are used to identify genetic variations. Genome sequencing became more cost-effective over time, and made it applicable in the medical field, allowing scientists to understand which genes are attributed to specific diseases.