Finishing the euchromatic sequence of the human genome. Venter, J. Science , — link to article. Pufferfish and Ancestral Genomes. Simple Viral and Bacterial Genomes. Complex Genomes: Shotgun Sequencing. DNA Sequencing Technologies. Genomic Data Resources: Challenges and Promises. Transcriptome: Connecting the Genome to Gene Function. Behavioral Genomics. Comparative Methylation Hybridization.
Pharmacogenomics and Personalized Medicine. Sustainable Bioenergy: Genomics and Biofuels Development. Citation: Chial, H. Nature Education 1 1 Thanks to the Human Genome Project, researchers have sequenced all 3. How did researchers complete this chromosome map years ahead of schedule? Aa Aa Aa. Phases of the Human Genome Project. The total is the sum of finished sequence red and unfinished draft plus predraft sequence yellow. Nature , Figure Detail.
The BAC library is represented by short, disordered, squiggly black line segments. Next, the clones are organized and mapped into overlapping large clone contigs. One of the BAC clones is randomly chosen for sequencing. It is fragmented into small pieces, which are subcloned into vectors to generate shotgun clones. These clones are then sequenced. Overlapping portions of the shotgun sequences are assembled to determine the genomic sequence.
Figure 3: Levels of clone and sequence coverage. Minimally overlapping clones are picked from a fingerprint clone contig for sequencing. The clones are sequenced to at least draft coverage to form a sequenced-clone contig. The sequences are then merged and ordered to create a sequence-contig scaffold. Celera: Shooting at Random and Organizing Later. Figure 4: Architecture of Celera's two-pronged assembly strategy. Figure 5: Anatomy of whole-genome assembly. In whole-genome assembly, the BAC fragments red line segments and the reads from five individuals black line segments are combined to produce a contig and a consensus sequence green line.
The contigs are connected into scaffolds, shown in red, by pairing end sequences, which are also called mates. If there is a gap between consecutive contigs, it has a known size. Next, the scaffolds are mapped to the genome gray line using sequence tagged site STS information, represented by blue stars. Figure 6: How to sequence DNA. This step produces a mixture of newly synthesized DNA strands that differ in length by a single nucleotide.
C The DNA mixture is separated by electrophoresis. D The electropherogram results show peaks representing the color and signal intensity of each DNA band. From these data, the sequence of the newly synthesized DNA strand is determined, as shown above the peaks. Dennis, C. Used with permission. Panel B shows nine newly synthesized DNA strands. Each of the strands differs in length by a single nucleotide and is labeled at the 3' end with a fluorescently-labeled ddNTP base.
Panel C shows the electrophoresis results. The DNA strands have been separated by size and appear as columns of colored bands. Panel D shows the electropherogram results, which are a series of colored peaks, with red representing T, black representing G, blue representing C, and green representing A. Shown above the peaks is the DNA sequence. From Rough Draft to Final Form. During this phase, the researchers filled in gaps and resolved DNA sequences in ambiguous areas that were not solved during the shotgun phase.
The final form of the human genome contained 2. Furthermore, the IHGSC reduced the number of gaps by fold; only gaps out of , gaps remained. The remaining gaps were associated with technically challenging chromosomal regions.
Although the earlier draft publications had predicted as many as 40, protein-encoding genes, the finishing phase reduced this estimate to between 20, and 25, protein-encoding genes.
Even so, the project took ten years to complete; the first draft of the human genome was announced in June In February , the publicly funded Human Genome Project and the private company Celera both announced that they had mapped virtually all of the human genome, and had begun the task of working out the functions of the many new genes that were identified. Scientists were surprised to find that humans only have around 25, genes, not much more than the roundworm Caenorhabditis elegans, and fewer than a tiny water crustacean called Daphnia, which has around 30, However, genome sequencing was making it clear that an organism's complexity is not necessarily related to its number f genes.
Also, while we might have a surprisingly small number of genes, they are often expressed in multiple and complex ways. Numerous genes have as many as a dozen different functions and may be translated into several different versions active in different tissues. So even though the puffer fish Tetraodon nigroviridis has more genes than we do—nearly 28,—the size of its entire genome is actually only around one tenth of ours as it has much less of the non-coding DNA. In April , the 50th anniversary of the publication of the structure of DNA, the complete final map of the Human Genome was announced.
Gene mapping Of the 25, or so human genes that have been identified as coding for proteins, most exist in several sequence variants, called alleles. Sometimes these variations are harmless. The gene that codes for eye colour has several alleles—one for blue eyes, another for brown eyes. Sometimes these genetic variations can cause a disease. For example, a mutation in the gene that transports ions across the membrane of lung cells can cause cystic fibrosis.
So, although our alleles may be different, all humans mostly share the same genes. The Human Genome Project identified the full set of human genes, sequenced them all, and identified some of the alleles, particularly those that can cause disease when they get mutated. Genes can be mapped relative to physical features of the chromosome, or relative to other genes.
The closer together the genes are, the more likely they are to stay together. Analysing how often genes become separated from each other can help establish the distance between genes, and produce a genetic linkage map.
In the Human Genome Project, the first task was to make a genetic linkage map for each chromosome. A genetic linkage map is made from studying patterns in gene separation, and shows the relative locations of genes on a chromosome. It does not tell us anything about the actual physical distances between the genes. A physical map, made by hybridizing a fluorescent-tagged probe to chromosomes, can be aligned with the linkage map. Molecular scale maps can be constructed from sequence markers in the DNA molecule, and quantifies these distances, usually in terms of how many base pairs there are between genes.
Together, the genetic linkage map, the physical map, molecular maps and sequence give us the complete picture of the genome. But how much significance does this have for our everyday lives? Actually, quite a lot. Identifying how our genes interact and which parts of our genome affect certain diseases and conditions has meant that doctors and scientists are able to better understand how these conditions work and how to treat them.
Drug treatments can be developed that are based on specific genetic mutations, and doctors may be able to diagnose a disease in a patient who is not showing typical symptoms. This can help avoid putting the patient through devastating chemotherapy treatments unnecessarily. These conditions could then be addressed with a preventative approach, before they take serious hold. A researcher reviews a DNA sequence.
There is no doubt that information from the Human Genome Project provides huge benefits to human health in helping to understand and treat genetic diseases such as breast cancer, cystic fibrosis and sickle cell anaemia.
Could genetic information be misused; for example, through genetic discrimination by employers or insurance companies? It is also worth noting that one aspect that attracted governmental support to the HGP was its potential for economic benefits. Even today, as budgets tighten, there is a cry to withdraw support from big science and focus our resources on small science. This would be a drastic mistake. Similarly to the HGP, significant returns on investment will be possible for other big science projects that are now under consideration if they are done properly.
It should be stressed that discretion must be employed in choosing big science projects that are fundamentally important. Clearly funding agencies should maintain a mixed portfolio of big and small science - and the two are synergistic [ 1 , 45 ]. So virtually every argument initially posed by the opponents of the HGP turned out to be wrong.
The HGP is a wonderful example of a fundamental paradigm change in biology: initially fiercely resisted, it was ultimately far more transformational than expected by even the most optimistic of its proponents. Since the conclusion of the HGP, several big science projects specifically geared towards a better understanding of human genetic variation and its connection to human health have been initiated.
These include the HapMap Project aimed at identifying haplotype blocks of common single nucleotide polymorphisms SNPs in different human populations [ 47 , 48 ], and its successor, the Genomes project, an ongoing endeavor to catalogue common and rare single nucleotide and structural variation in multiple populations [ 50 ]. Data produced by both projects have supported smaller-scale clinical genome-wide association studies GWAS , which correlate specific genetic variants with disease risk of varying statistical significance based on case—control comparisons.
Since , over 1, GWAS have been published [ 51 ]. Although GWAS analyses give hints as to where in the genome to look for disease-causing variants, the results can be difficult to interpret because the actual disease-causing variant might be rare, the sample size of the study might be too small, or the disease phenotype might not be well stratified.
Moreover, most of the GWAS hits are outside of coding regions - and we do not have effective methods for easily determining whether these hits reflect the mis-functioning of regulatory elements. The question as to what fraction of the thousands of GWAS hits are signal and what fraction are noise is a concern. Pedigree-based whole-genome sequencing offers a powerful alternative approach to identifying potential disease-causing variants [ 52 ].
Five years ago, a mere handful of personal genomes had been fully sequenced for example, [ 53 , 54 ]. Now there are thousands of exome and whole-genome sequences soon to be tens of thousands, and eventually millions , which have been determined with the aim of identifying disease-causing variants and, more broadly, establishing well-founded correlations between sequence variation and specific phenotypes.
For example, the International Cancer Genome Consortium [ 55 ] and The Cancer Genome Atlas [ 56 ] are undertaking large-scale genomic data collection and analyses for numerous cancer types sequencing both the normal and cancer genome for each individual patient , with a commitment to making their resources available to the research community.
We predict that individual genome sequences will soon play a larger role in medical practice. In the ideal scenario, patients or consumers will use the information to improve their own healthcare by taking advantage of prevention or therapeutic strategies that are known to be appropriate for real or potential medical conditions suggested by their individual genome sequence.
Physicians will need to educate themselves on how best to advise patients who bring consumer genetic data to their appointments, which may well be a common occurrence in a few years [ 57 ]. In fact, the application of systems approaches to disease has already begun to transform our understanding of human disease and the practice of healthcare and push us towards a medicine that is predictive, preventive, personalized and participatory: P4 medicine.
A key assumption of P4 medicine is that in diseased tissues biological networks become perturbed - and change dynamically with the progression of the disease. Hence, knowing how the information encoded by disease-perturbed networks changes provides insights into disease mechanisms, new approaches to diagnosis and new strategies for therapeutics [ 58 , 59 ]. Let us provide some examples. First, pharmacogenomics has identified more than 70 genes for which specific variants cause humans to metabolize drugs ineffectively too fast or too slow.
Third, in some cases, cancer-driving mutations in tumors, once identified, can be counteracted by treatments with currently available drugs [ 61 ]. And last, a systems approach to blood protein diagnostics has generated powerful new diagnostic panels for human diseases such as hepatitis [ 62 ] and lung cancer [ 63 ].
These latter examples portend a revolution in blood diagnostics that will lead to early detection of disease, the ability to follow disease progression and responses to treatment, and the ability to stratify a disease type for instance, breast cancer into its different subtypes for proper impedance match against effective drugs [ 59 ].
We envision a time in the future when all patients will be surrounded by a virtual cloud of billions of data points, and when we will have the analytical tools to reduce this enormous data dimensionality to simple hypotheses to optimize wellness and minimize disease for each individual [ 58 ]. The HGP challenged biologists to consider the social implications of their research. That process continues as different societal issues arise, such as genetic privacy, potential discrimination, justice in apportioning the benefits from genomic sequencing, human subject protections, genetic determinism or not , identity politics, and the philosophical concept of what it means to be human beings who are intrinsically connected to the natural world.
Strikingly, we have learned from the HGP that there are no race-specific genes in humans [ 65 — 68 ]. There remain fundamental challenges for fully understanding the human genome. The question of what information these regions contain is a fascinating one.
In addition, there are highly conserved regions of the human genome whose functions have not yet been identified; presumably they are regulatory, but why they should be strongly conserved over a half a billion years of evolution remains a mystery.
There will continue to be advances in genome analysis. Developing improved analytical techniques to identify biological information in genomes and decipher what this information relates to functionally and evolutionarily will be important. Developing the ability to rapidly analyze complete human genomes with regard to actionable gene variants is essential. It is also essential to develop software that can accurately fold genome-predicted proteins into three dimensions, so that their functions can be predicted from structural homologies.
Likewise, it will be fascinating to determine whether we can make predictions about the structures of biological networks directly from the information of their cognate genomes. While we have become relatively proficient at determining static and stable genome sequences, we are still learning how to measure and interpret the dynamic effects of the genome: gene expression and regulation, as well as the dynamics and functioning of non-coding RNAs, metabolites, proteins and other products of genetically encoded information.
The practice of systems biology begins with a complete parts list of the information elements of living organisms for example, genes, RNAs, proteins and metabolites.
The goals of systems biology are comprehensive yet open ended because, as seen with the HGP, the field is experiencing an infusion of talented scientists applying multidisciplinary approaches to a variety of problems. Integrating these data allows the creation of models that are predictive and actionable for particular types of organisms and individual patients.
These goals require developing new types of high-throughput omic technologies and ever increasingly powerful analytical tools. The HGP infused a technological capacity into biology that has resulted in enormous increases in the range of research, for both big and small science. Experiments that were inconceivable 20 years ago are now routine, thanks to the proliferation of academic and commercial wet lab and bioinformatics resources geared towards facilitating research.
In particular, rapid increases in throughput and accuracy of the massively parallel second-generation sequencing platforms with their correlated decreases in cost of sequencing have resulted in a great wealth of accessible genomic and transcriptional sequence data for myriad microbial, plant and animal genomes. These data in turn have enabled large- and small-scale functional studies that catalyze and enhance further research when the results are provided in publicly accessible databases [ 70 ].
One descendant of the HGP is the Human Proteome Project, which is beginning to gather momentum, although it is still poorly funded. This exciting endeavor has the potential to be enormously beneficial to biology [ 71 — 73 ].
The Human Proteome Project aims to create assays for all human and model organism proteins, including the myriad protein isoforms produced from the RNA splicing and editing of protein-coding genes, chemical modifications of mature proteins, and protein processing. The project also aims to pioneer technologies that will achieve several goals: enable single-cell proteomics; create microfluidic platforms for thousands of protein enzyme-linked immunosorbent assays ELISAs for rapid and quantitative analyses of, for example, a fraction of a droplet of blood; develop protein-capture agents that are small, stable, easy to produce and can be targeted to specific protein epitopes and hence avoid extensive cross-reactivity; and develop the software that will enable the ordinary biologist to analyze the massive amounts of proteomics data that are beginning to emerge from human and other organisms.
Newer generations of DNA sequencing platforms will be introduced that will transform how we gather genome information. Third-generation sequencing [ 74 ] will employ nanopores or nanochannels, utilize electronic signals, and sequence single DNA molecules for read lengths of 10, to , bases. Third-generation sequencing will solve many current problems with human genome sequences.
First, contemporary short-read sequencing approaches make it impossible to assemble human genome sequences de novo ; hence, they are usually compared against a prototype reference sequence that is itself not fully accurate, especially with respect to variations other than SNPs.
This makes it extremely difficult to precisely identify the insertion-deletion and structural variations in the human genome, both for our species as a whole and for any single individual.
The long reads of third-generation sequencing will allow for the de novo assembly of human and other genomes, and hence delineate all of the individually unique variability: nucleotide substitutions, indels, and structural variations. Second, we do not have global techniques for identifying the 16 different chemical modifications of human DNA epigenetic marks, reviewed in [ 75 ]. It is increasingly clear that these epigenetic modifications play important roles in gene expression [ 76 ].
Thus, single-molecule analyses should be able to identify all the epigenetic marks on DNA. Third, single-molecule sequencing will facilitate the full-length sequencing of RNAs; thus, for example, enhancing interpretation of the transcriptome by enabling the identification of RNA editing, alternative splice forms with a given transcript, and different start and termination sites.
Last, it is exciting to contemplate that the ability to parallelize this process for example, by generating millions of nanopores that can be used simultaneously could enable the sequencing of a human genome in 15 minutes or less [ 77 ].
The interesting question is how long it will take to make third-generation sequencing a mature technology. The HGP has thus opened many avenues in biology, medicine, technology and computation that we are just beginning to explore. Hood L: Acceptance remarks for Fritz J.
Russ Prize. The Bridge. Google Scholar. Dulbecco R: A turning point in cancer research: sequencing the human genome.
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