File Name: genomics and proteomics principles technologies and applications .zip
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The author's overview highlighted the recent history of the two fields and laid the foundation for the rest of the symposium presentations. The announcement of the completion of the draft sequencing of the human genome in the spring of signaled a watershed event in biology.
It had long been recognized that on one end of a spectrum, diseases like cystic fibrosis are almost entirely caused by a genetic factor. Simply the presence of a particular mutation in both of the alleles of a single gene leads to the disease. On the other end of that spectrum, infectious diseases, such as the acquired immunodeficiency syndrome, are predominantly caused by environmental factors.
Yet even for these environmentally caused diseases, genetically determined host factors affect the individual's response to the illness. The majority of human diseases falls somewhere between these extremes. Diseases like diabetes and heart disease comprise a mix of both genetic and environmental factors, and the genetic component is often complex, involving many genes.
By the mids it was clear that DNA sequence could expedite our understanding of the genetic component of disease, help us to understand its pathophysiology, and identify potential targets for treatment. A second key factor was the development of tools that suggested the feasibility of the project. By the mids, the Sanger sequencing method, 5 along with some technical improvements in the enzymology of DNA polymerases 6 and the labeling of the nucleotides, 7 had advanced to the point at which sequencing advocates could dream of completing the entire human genome.
Even with these tools, however, it was by no means obvious that this was achievable. After all, if the genome is considered the Book of Life, it is a big book. There are more than 3 billion letters in the human genome. At the time the project was conceived, typical sequencing read lengths were in the — base range. So with a simple calculation of the number of needed reads and the amount of computing power needed to handle the data not to mention special technical problems such as repeated sequences , it is not surprising that many argued that it would be an inappropriate use of research money to take on this project and perhaps mere folly altogether.
Nevertheless, the draft version of the human sequence has been completed far ahead of the many decades originally anticipated. More than 20 different major sequencing centers and hundreds of scientists participated in this project. In the final stages of the project, centers were sequencing 24 hours a day, 7 days a week, all across the globe. The HGP was not always a high-throughput sequencing project.
That aspect of its operations did not begin until late in the s after an early phase during which genetic maps and technologies were developed that were essential to the high-throughput sequencing that occurred at the end.
Moreover, the HGP has shown us that the technologies themselves are at least as powerful as the data collected. New tools, such as DNA microarrays and transcriptional profiling, serial analysis of gene expression, and haplotype mapping, will prove to revolutionize the way that important genes are identified and diseases are characterized.
Although the data are increasing rapidly, the sequencing of the human genome is not yet complete. As of this printing, more than Several chromosomes are either completed or are near completed, including 20, 21, 22, 10, 13, 14, 19, 6, and 7. Although the HGP will impact all aspects of biology, some areas in particular will be directly influenced as indicated in Table 1. Not surprisingly, advances in our understanding of genome structure and function and human evolution are the two disciplines most immediately affected by the genome project.
The manuscripts describing the draft sequencing dwelt in these areas at length. Moreover, there has been an unprecedented acceleration in the number of papers published about human evolution and genome structure in the last several years.
The sequence is a rich information store that will be mined for years, becoming even more fruitful as additional vertebrate genomes are completed, such as the mouse, the rat, and the dog. By providing a foundation upon which discovery can occur, the genome sequence is also impacting the daily execution of biomedical research. This is occurring in two key areas: first, genetic approaches are used to create linkages that demonstrate genes that play a role in the etiology of disease; and second, by providing a template that can be used to produce and study the gene products themselves in order to understand the biochemical mechanisms that play a direct role in the causation and treatment of disease.
Finally, on the coming horizon, the genome will have a large effect on the practice of medicine. These profound changes will also demand a careful consideration of the public policy and ethics regarding the use of genetic information. Among the first lessons learned from the genome sequencing was the level of heterogeneity in the genome. Features of chromosome 7 from the draft sequence published by the International Human Genome Sequencing Consortium.
Genetic variation also appears to be heterogeneous across the genome. Single-nucleotide polymorphisms, or SNPs, indicate positions in the genome where there are common single base genetic differences in the population. Peaks in this plot indicate places where SNPs are common in the genome and, as indicated, some areas contain frequent polymorphisms whereas other areas much less so.
Gene density is also heterogeneous in the genome. As many genes have not yet been identified, gene density has been estimated by four different techniques. One method for estimating the presence of genes is to look for regions of homology with the genome of the pufferfish T. A second method of gene prediction is to evaluate the frequency of expressed sequence tags ESTs , which is a measure of RNA species with poly-A tails and thus should correlate with genes in the genome.
Third, the starts of genes are also marked. Finally, the dinucleotide CpG occurs much less frequently in the human genome than would be predicted by the known fraction of Cs and Gs.
Moreover, the notion that gene density correlates with GC content is supported by this analysis of the genome. The heterogeneity of gene density is evident even at the chromosomal level. Chromosome 21 Fig. Down syndrome, or trisomy 21, is one the few situations in which an extra copy of an entire chromosome in humans is compatible with life.
Perhaps the reason that trisomy 21 is tolerated is that this chromosome has relatively few genes. Variation in gene density on different chromosomes.
This may partially explain why trisomy 21 is not lethal. Reprinted by permission from Nature, copyright , Macmillan Publishers Ltd. One of the most celebrated surprises of the genome was the number of genes that were predicted from the genome. The predicted number of genes in the human genome in comparison with other genomes.
The explanation for this apparent paradox resides at the level of the gene products, not the genes, that is, an organism's complexity is determined by its proteins and their various forms and regulations.
At the protein level, humans and other vertebrates possess a significant level of diversity. First, humans appear to have many more splice variants per gene than simpler eukaryotes. An analysis of reconstructed mRNAs for chromosomes 22 and 19 suggests that on average there are at least three transcripts per gene in humans compared with the worm, which appears to have about 1.
Taking this into account, the number of different mRNA species in the human may exceed 90, compared with fewer than 30, in the worm. Second, proteins in humans have greater architectural complexity than their counterparts in simpler organisms. Proteins are composed of distinct structural domains, usually ascertained by sequence homology among related proteins both within and between species.
These domains often impart particular biochemical functions on proteins that contain them, such as a catalytic activity or the ability to interact with a corresponding domain on another protein. In the tabulation so far which may be an underestimate , humans have nearly twice as many distinct domain architectures as the worm and fly, and almost 6 times as many as yeast. Moreover, when examining protein homologs over years of evolution, human proteins tend to be more complex and contain more domain architectures per protein than simpler organisms.
The availability of a greater selection of domain architectures, along with a more complex assembly of those domains into multidomain proteins, will undoubtedly lead to significantly greater variety of protein function in humans.
Thus, whereas the number of genes may not be dramatically larger in humans and other vertebrates, the complexity of the proteome and its vast catalog of activities is remarkably greater.
Moreover, this complexity is likely to increase further when considering the regulation of protein levels and activities by both transcriptional and posttranscriptional mechanisms, and by the ability of proteins to interact with one another in a combinatorial amplification of different activities.
Elucidating this regulation and these interactions is one of the great challenges of the Proteomic Era. One of the most fertile areas of research arising from the genome project has been the study of human evolution.
This molecular archaeology has exploited the frequency of repeat sequences that appear in the genome Table 2. These repeat sequences can be dated using several different techniques. In particular, the transposon repeats are useful because transposons must have contained functional elements when they first inserted into the chromosome and therefore their original starting sequence can be deduced.
Any changes that are observed in the actual sequence compared with the predicted sequence presumably represent mutations occurring in DNA that is not under selection pressure. Thus the rate of mutation can be used to date the transposons, some of which we now know date back million years. This deep fossil record can tell us much about our history. For unapparent reasons, the human genome retains them for a long time. Also, surprisingly, transposon activity has fallen dramatically in the last 50 million years.
There are fewer than predicted new transposons in the human genome during this period. Some repeat sequences may actually provide an evolutionary advantage. The most common repeat sequences in the genome are called Alu sequences. Historically, Alu repeats have been regarded as irritants by those who have done positional gene cloning because they kept appearing in sequencing runs.
They can be dated by their sequence divergence in a manner similar to that used for the transposon elements. The Alu sequences can then be grouped according to their age and examined for their relative abundance in different parts of the genome Fig.
As noted above, chromosomal areas with high GC content tend to have higher gene density. Whereas Alu sequences that arrived more than 60 million years ago are located predominantly in the GC rich regions, Alu sequences that have arrived in the genome relatively recently, that is, in the last million years, are more abundant in the gene-poor, AT-rich regions.
Thus, when new Alu repeats enter the genome, they tend to enter the genome in the gene-poor areas and over time disappear from the genome.
However, when an Alu repeat occasionally hits into a gene-rich area, it has a relatively greater tendency to remain. Thus there may be a selective advantage to having an Alu repeat in a gene-rich area.
It is not yet clear what that advantage is. The age and frequency of Alu sequences in the humane genome.
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Proteomics: methodologies and applications to the study of human diseases. Correspondence to. Recent advances of methodologies in this field have opened new opportunities to obtain relevant information on normal and abnormal processes occurring in the human body. In the current report, the main proteomics techniques and their application to human disease study are reviewed.
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. T he nature of biological inquiry and the norms of behavior in the scientific community have changed in the wake of the Human Genome Project HGP and the birth of proteomics. Complementing the traditional hypothesis-driven study of single genes or proteins is the option of studying many genes or proteins simultaneously.
Get this from a library! Please use one of the following formats to cite this article in your essay, paper or report: APA Smith, Yolanda. Free Book Principles Of Proteomics Uploaded By Nora Roberts, principles of proteomics second edition provides a concise and user friendly introduction to the diverse technologies used for the large scale analysis of proteins as well as their applications and their impact in areas such as drug discovery agriculture and the fight against Proteomics Technologies and Applications reviews and describes the nature and application of molecules with proteins or peptides, and elucidates and predicts the possible molecular and physiological causes related to changing proteomic profiles. Genomics and proteomics : principles, technologies, and applications. Format: PDF, Kindle View: Get Books Principles of Proteomics, Second Edition, provides a concise and user-friendly introduction to the diverse technologies used for the large-scale analysis of proteins, as well as their It's nearly what you obsession currently.
Genomics can be broadly defined as the systematic study of genes, their functions, and their interactions. Analogously, proteomics is the study of proteins, protein complexes, their localization, their interactions, and posttranslational modifications. Some years ago, genomics and proteomics studies focused on one gene or one protein at a time. With the advent of high-throughput technologies in biology and biotechnology, this has changed dramatically. We are currently witnessing a paradigm shift from a traditionally hypothesis-driven to a data-driven research.
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