Initial sequencing and analysis of the human genome
Dec 07, · How much did it cost to sequence a human genome in (i.e., roughly a decade ago)? Since the completion of the HGP and the generation of the first 'reference' human genome sequence, efforts have increasingly shifted to the generation of human genome sequences from individual people. Whole genome sequencing (WGS), also known as full genome sequencing, complete genome sequencing, or entire genome sequencing, is the process of determining the entirety, or nearly the entirety, of the DNA sequence of an organism's genome at a single time. This entails sequencing all of an organism's chromosomal DNA as well as DNA contained in the mitochondria and, for plants, in the .
Advances in the field of genomics over the past quarter-century have led to substantial reductions in the cost of genome sequencing. The underlying costs associated with different methods and strategies for sequencing genomes are of great interest because they influence the scope and scale of almost all genomics research projects.
Significant scrutiny and attention have been given to genome-sequencing costs and how they are calculated since the beginning of the field of genomics in the late iy. For example, NHGRI has carefully tracked costs per genome at its funded 'genome sequencing centers' for many years see Figure 1.
With the growing scale of human genetics studies and the increasing number of clinical applications for genome sequencing, even greater attention is being paid to understanding the underlying costs of generating a human genome sequence. Accurately determining the cost for sequencing a given genome e. There are many parameters to define and nuances to consider. In fact, it is difficult to cite precise genome-sequencing cost figures that mean the same thing to all people because, in reality, different researchers, research institutions, and companies typically track and account for such costs in different fashions.
A genome consists of all of the DNA contained in a cell's nucleus. DNA is composed of four chemical building blocks or "bases" for simplicity, abbreviated G, A, T, and Cwith the cosy information encoded within DNA determined by the order sequende those bases.
Diploid organisms, like doed and all other mammals, contain duplicate copies of almost how much does it cost to have a surrogate of their DNA i. The size of an organism's genome is generally considered to be the total number of bases in one representative copy of its nuclear DNA.
In the case of diploid organisms like humansthat corresponds to the sum of the sizes of one copy of each chromosome pair. Organisms generally differ in their genome sizes. For example, the genome of E. Obviously, the cost to sequence a genome depends on its size. Genomes are large and, at least with today's methods, their bases cannot be 'read out' in order i.
Rather, to sequence a genome, its DNA must first be broken down into smaller pieces, with each resulting piece then subjected to chemical reactions that allow cot identity and order of its bases to be deduced. The established base order derived from each piece of DNA is often called a 'sequence how to get the old yahoo messenger back and the collection of the resulting set of sequence reads often numbering in the billions is then computationally assembled back together to deduce the sequence of the starting genome.
Sequencing human genomes are nowadays aided by the availability of available 'reference' sequences of the human genome, which play an important role in the computational assembly process. Historically, the process of breaking down genomes, sequencing the individual pieces of DNA, and then reassembling the individual sequence reads to generate a sequence of the starting genome was called 'shotgun sequencing' although this terminology is used less tl today.
When an entire genome is being sequenced, the process is called 'whole-genome gdnome. An alternative to whole-genome sequencing is gehome targeted sequencing of part of a genome.
Most often, this involves just sequencing the protein-coding regions of a genome, which reside within DNA segments called 'exons' and reflect the currently 'best understood' part of most genomes. Methods are now readily available to experimentally 'capture' or isolate just the exons, which can then be sequenced to generate a 'whole-exome sequence' of a genome. But since much less DNA is sequenced, whole-exome sequencing is at least currently cheaper than whole-genome sequencing.
Another important doe of the costs associated with genomme genome sequences relates to data quality. That quality is heavily dependent upon the average number of times each base in the genome is muxh 'read' during the sequencing process. Producing truly high-quality 'finished' sequence by this definition is very expensive; of note, the process of 'sequence finishing' is very labor-intensive and mudh thus associated with high costs.
In fact, most human genome sequences produced today are 'draft sequences' sometimes how to tracking number phone and sometimes below the accuracy defined above. There are thus a number of factors to consider when calculating the costs associated with genome sequencing. There are multiple different types and quality levels of genome sequences, and there can be many steps and activities involved in the process itself.
Understanding the true cost of a genome sequence therefore requires knowledge about what was and was not included in calculating that cost e. In reality, seuence are often differences in what gets included when estimating genome-sequencing costs in different situations. Below is summary information about: 1 the estimated cost of sequencing the first human genome as part of the HGP; 2 the estimated cost of sequencing a human genome in i.
The HGP involved first mapping and then sequencing the human genome. Ut former was required at the time because there was otherwise no 'framework' for organizing the actual sequencing or hhman resulting sequence data.
The maps of the human genome served as 'scaffolds' on which to connect individual segments of assembled DNA sequence. These genome-mapping efforts were quite expensive, but were essential at the time for generating an accurate genome sequence.
It is difficult to estimate the costs associated with the 'human genome mapping phase' of the HGP, but it was certainly in the many tens of millions of dollars and probably hundreds of millions of dollars. Once significant human genome sequencing began for the HGP, a 'draft' human genome sequence as described above was produced over a month period genomr April to June The HGP then proceeded to refine the 'draft' and produce a 'finished' human genome sequence as described abovewhich was achieved by Of note, generating the final human genome sequence by the HGP also relied on the sequences of small targeted regions of the human genome that were generated before the HGP's main production-sequencing phase; it is impossible to estimate the costs associated with these various other genome-sequencing efforts, but they likely total in the tens of millions of dollars.
The above explanation illustrates the difficulty in coming up with a single, accurate number for the cost of generating that first human genome sequence as part of the HGP. Such a calculation requires a clear delineation about what does and does not get 'counted' in the estimate; further, most of the cost estimates for individual components can only be given how many monitors does windows xp support ranges.
The truth is likely somewhere in between. The above estimated cost for generating the first human genome sequence by the HGP should not be confused with the total cost of the HGP.
The originally projected cost for the U. But the latter number sequenxe the total U. Further, this amount does not reflect the additional funds for an overlapping set of what was the major issue of the lincoln douglas debates pursued sequencf other countries that participated in the HGP. As the HGP was nearing completion, genome-sequencing pipelines had benome to the point that NHGRI was able to collect fairly seauence cost information from the major sequencing centers funded by the Institute.
Since the completion of the HGP and the generation of the first 'reference' human genome sequence, efforts sequennce increasingly shifted to the generation of human genome sequences from individual people.
Thus, the generation of a person's genome sequence is a notably different endeavor than what the HGP did. Within a few years following the end of tl HGP e. While revolutionary new DNA sequencing technologies, such as those in use today, were not quite implemented at that time, genomics groups continued to refine the basic methodologies used during the HGP and muh lowering the costs for genome ro. Considerable efforts were dpes made to benome sequencing of nonhuman genomes much more so than human genomesbut the cost-accounting data collected at that time can be used to estimate the approximate cost that would have been associated with human genome sequencing at that time.
The decade following mcuh HGP brought revolutionary advances in DNA sequencing technologies that are fundamentally changing the nature of genomics. So-called 'next-generation' DNA sequencing methods arrived on the scene, and their effects quickly became evident in terms of lowering genome-sequencing costs ; note that these NHGRI-collected data are 'retroactive' fo nature, and do not always accurately reflect the 'projected' costs for genome sequencing going forward.
In genoe, the most common routine for sequencing an individual's human genome involves generating a 'draft' sequence and comparing it to a reference human genome sequence, so as to catalog all sequence variants in that genome; such a routine does not involve any sequence finishing.
In short, nearly all human genome sequencing in yields high-quality 'draft' but unfinished sequence. The quality of the resulting 'draft' sequences is heavily dependent on the amount of average base redundancy provided by the generated data with higher redundancy costing more. Adding to the complex landscape of genome sequencing in has been the emergence of commercial enterprises offering genome-sequencing services at competitive pricing. Direct comparisons between commercial versus academic genome-sequencing operations can be particularly challenging because of the many nuances about what each includes in any cost estimates with such details often not revealed by private companies.
The cost data that NHGRI collects from its funded genome-sequencing groups includes information about a wide range of activities and components, such as: reagents, consumables, DNA-sequencing instruments, certain computer equipment, other equipment, laboratory pipeline development, gebome information management systems, initial data processing, submission of data to public databases, project management, utilities, other indirect costs, labor, and administration. Almost certainly, companies vary in terms of which of the items in the above lists get included in any cost estimates, making direct cost comparisons with academic genome-sequencing groups difficult.
It is thus important to consider these variables - along with geno,e distinction between retrospective umch projected costs - when comparing genome-sequencing costs claimed genomr different groups. Anyone comparing costs for genome sequencing should also buman aware of genoe distinction between 'price' and 'cost' - a given price may be either hoq or lower than the actual cost.
Commercial prices for whole-genome and whole-exome sequences have often but not always been slightly below these numbers. Innovation in genome-sequencing technologies and strategies does not appear to be slowing.
As a result, one can readily expect continued reductions in the cost for human genome sequencing. The key factors to consider when assessing the 'value' associated with gnome estimated cost for generating a human genome sequence - in particular, the amount of the genome whole versus exomequality, and associated data analysis if any - will likely remain largely the same.
With new DNA-sequencing platforms anticipated in the coming years, the nature of the generated sequence data and the associated costs will likely continue to be dynamic. As such, continued attention will need to be paid to the way in which the costs associated with genome sequencing are calculated.
The Cost of Sequencing a Human Genome. Overview Significant scrutiny and attention have been given to genome-sequencing costs and how they are calculated since the beginning of the field of genomics in the late s. How much does it cost to sequence human genome Primer on Genome Sequencing. Timeline of Costs. How much did it cost to generate the first human genome sequence as part of the Human Genome Project?
How much did it cost to sequence sequencce human genome in i. How much does it cost to sequence a human genome in i. Timeline of Costs How much did it cost to generate the first human genome sequence as part of the Human Genome Project? Guman Ahead. Looking Ahead Innovation in genome-sequencing technologies and strategies does not appear to be slowing.
Kris A. Wetterstrand, M. Last updated: December 7,
Actually, quite a lot. As the cost of sequencing a genome plummets—the first human genome sequenced in cost somewhere in the order of US$ billion, while it can now be done for less than US$1,—doctors have a new and extremely powerful tool at their disposal. Feb 15, · In , genome scientists considered a proposal 38 that would have involved producing a draft genome sequence of the human genome in a first phase and then returning to finish the sequence . Jan 06, · In reality, in order to sequence a whole human genome, you need to generate a bunch of short “reads” (~ base pairs, depending on the platform) and then “align” them to the reference genome.
The data and research currently presented here is a preliminary collection or relevant material. We will further develop our work on this topic in the future to cover it in the same detail as for example our entry on World Population Growth.
If you have expertise in this area and would like to contribute, apply here to join us as a researcher. Below I will show how aspects as diverse as processing speed, product price, memory capacity, and even the number and size of pixels in digital cameras have also been progressing exponentially. The law was described as early as by the Intel co-founder Gordon E. Moore after whom it is named.
As our large updated graph here shows, he was not only right about the next ten years but astonishingly the regularity he found is true for more than half a century now. Note the logarithmic vertical axis chosen to show the linearity of the growth rate.
The line corresponds to exponential growth with the transistor count doubling every two years. In the following, I show that technological developments in many respects are growing exponentially. But in and of itself, the doubling of transistors every two years does not directly matter in our lives. Therefore, I ask in which ways the exponential growth of technology matters and will give an overview of how the exponential technological advancement is a driver of technological and social change that very much matters for our lives now.
More importantly for us is that the power and speed of computers increased exponentially; the doubling time of computational capacity for personal computers was 1. The increasing power of a wider range of computers — starting with the first general purpose computer ENIAC in — is shown in the black and white chart. We also show this series in interactive form, updated to the year Here, the growth of supercomputer power is measured in terms of the number of floating-point operations carried out per second FLOPS by the largest supercomputer in any given year.
FLOPS are a measure of calculations per second for floating-point operations. Floating-point operations are needed for very large or very small real numbers, or computations that require a large dynamic range. It is therefore a more accurate measured than simply instructions per second. Whilst some technological change follows a continued linear progression, many of the technological innovations we see follow a non-linear pathway.
This non-linearity is observed most clearly in examples which show rapid evolution following an important enabling innovation. Below we have included two examples of such trends: the take-off of human flight, and the sequencing of the human genome. This chart shows the global distance record set by non-commercial flights since This record represents the maximum distance a non-commercial powered aircraft has traveled without refueling. We see that prior to , humans had not yet developed the technology necessary to enable powered flight.
This initial innovation sparked continued, rapid progress in modern aviation, with the record distance increasing nearly ,fold from 0. This provides one examples of non-linear evolution of technological change: a single enabler shifted us from a civilization unable to fly, to one which could. Progress in aviation — and space exploration — has been rapid since.
Another example which demonstrates this non-linear progress is the field of human genome DNA sequencing. This initial discovery and determination of the human genome sequence was a crucial injection point in the field of DNA sequencing.
Note that this costing refers to the price of raw base pairs of DNA sequence; the cost of producing the full human genome is higher than the sum of 30 million base pairs would suggest. This is because some redundant sequence coverage would be necessary to complete and assemble the full genome. Nonetheless, this rapid decline in cost is also observed in prices for the sequencing of a complete human genome. Increasing computational power — and increasing product quality — matters indeed more than a mere doubling of transistors.
But if the technologically-advanced products are prohibitively expensive then they can only have a limited impact on the whole society. For this reason, it is interesting to look at both the product quality and the price. The author and inventor Ray Kurzweil analyzed the change of price and quality for computing machines since He not only analyzed the improvements of integrated circuits but also looked at the predecessors — earlier transistors, vacuum tubes, relays and electromechanical computers.
What he found is that Moore did not only make a valid prediction of the future, but his description is also valid for the past! The exponential growth rate that Moore picked up in the s was driving technological progress since the beginning of the century. It is especially insightful if one wants to understand how technological progress mattered as a driver of social change.
The extension of the time frame also makes clear how our modern computers evolved. It is an insightful way of understanding that the computer age really is the successor to the Industrial Revolution. One could also view the previous graph as a function of price instead of calculations per second; in this view you would find an exponentially decreasing price for a given product quality over years.
The implication of this rapid simultaneous improvement in quality and decrease of the product price is that, according to a detailed discussion on reddit here , a current laptop May has about the same computing power as the most powerful computer on earth in the mid s. In the chart shown we see the price changes in goods and services in the United States from , measured as the percentage price change since Positive values indicate an increase in prices since , and negative values represent a price decline.
Here we see a distinct divide between consumer durables and technologies which have typically seen a price decline , and service-based purchases which have increased in price. Examples of service-based roles such as nursing, healthcare, childcare and education have experienced little productivity growth relative to manufacturing sectors which have seen continued improvements through technological innovation. In order to retain employees in service-based roles, salaries have risen in order to remain competitive with industrial sectors; this increase in pay has occurred despite minimal gains in productivity.
This may in part explain why the cost of education, healthcare and other services have risen faster than the general rate of inflation. The cost to keep the machine running also matters.
Electrical efficiency measures the computational capacity per unit of energy, and it is also important with respect to the environmental impact that energy production has. The progress in this respect has been tremendous: researchers found that over the last six decades the energy demand for a fixed computational load halved every 18 months. In this chart we see the computing efficiency of various processors over time. Here, computing efficiency is measured as the number of watts a measure of electrical power needed to carry out a million instructions per second Watts per MIPS.
Looking at these two picturesit becomes immediately clear how fast technological progress increased the storage capacity. Considering the time since the introduction of the IBM in , the growth rate of storage capacity has not been as constant as for the other measures discussed before. Early on, technological revolutions boosted the capacity stepwise and not linearly. Yet for the time since , progress has been very steady and at an even higher rate than the increase of computer speed — as shown in the chart here.
Exponentially advancing technological progress can not only be found in computing machines. Cameras are a different example: for a given price consumers can buy cameras with more and more pixels. The number of pixels has again exponentially increased, as seen in the graph here. The exponential growth rates that we have observed over the last decades seem to promise more exciting technological advances in the future. Many other types of technology have seen exponential growth rates beyond the ones discussed above.
If this growth rate should remain constant, it leads to some mind-bending opportunities. Coronavirus pandemic : daily updated research and data. Notice: This is only a preliminary collection of relevant material The data and research currently presented here is a preliminary collection or relevant material. The exponential increase of the number of transistors on integrated circuits. Click to open interactive version. Computational power: operations per second.
Exponentially increasing computational capacity over time computations per second — Koomey, Berard, Sanchez, and Wong 4. The non-linearity of technological change. Exponential technological progress goes with exponential decreases in costs. Decreasing prices of consumer durables: technological innovation and economies of scale. Exponential increase of computer memory — exponentially increasing storage capacity and decreasing storage costs.
Exponential growth of pixels per Australian dollar, — Wikipedia The future of exponential technological growth. Number of digits in the largest known prime since computers started looking for them, — Wikipedia Wordpress Edit Page.
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