News
Nature Biotechnology - 25, 149 (2007) Published online: 1 February 2007; doi:10.1038/nbt0207-149
Catherine Shaffer
Ann Arbor, Michigan
Growing demand in both the research and clinical markets is fueling the development – and funding – of more efficient genomic sequencing methods.
"The market for next generation sequencing technology already stands at $1 billion driven largely by resequencing efforts." John Sullivan Leerink SwanBoston
On January 8, Solexa, of Hayward, California, announced the completion of an early-access program evaluating its next-generation Genome Analysis system with customers and reiterated its intention to begin full commercial sales this quarter. Two months earlier, in anticipation of the entry of Solexa's technology and wanting a piece of the emerging market for whole-genome resequencing and analysis, San Diego, California–based microarray maker Illumina announced its intention to acquire the firm in a stock-for-stock transaction valued at around $600 million (Nat. Biotechnol. 25, 10, 2007).On the completion of the merger, the new Solexa-Illumina business combination will join several other companies currently pushing the boundaries of sequencing technology. Curagen spin-off 454 Life Sciences, of Branford, Connecticut, and Agencourt Personal Genomics in Beverly, Massachusetts, a part of Applied Biosystems Group, are already on the market with systems that bring sequencing costs down several orders of magnitude below the millions of dollars per genome cost associated with capillary-array electrophoresis (CAE) sequencing—the technology that made possible the Human Genome Project a mere six years ago. Cambridge, Massachusetts–based Helicos Biosciences, for its part, claims that its single-molecule sequencing technology, expected to debut in the second half of the year, will enable the sought-after '$1,000 genome' price point, although not immediately. Smaller companies are also merging their respective technologies in an attempt to stay competitive in this technology race.The intense activity in part stems from a pent-up and growing demand in both the research and clinical markets—the dynamic that Illumina identified in its discussions with customers, leading to the bid for Solexa. Indeed, the field appears to be advancing more rapidly than originally envisioned. According to John Sullivan, equity research analyst at Leerink Swann in Boston, the market for next-generation sequencing technology already stands at $1 billion, driven largely by targeted resequencing efforts aimed at finding genetic variations and rare mutations that contribute to complex diseases.
In 2004, the National Human Genome Research Institute (NHGRI) proposed a way to achieve affordable human genome sequencing by 2014, in two increments. NHGRI program director Jeff Schloss explains: "The way the [Requests for Applications] were laid out, at the time we launched the program, we were hoping the $100,000 genome might come in five years. The goal for $1,000 was to be five years after that." Solexa has already sequenced a gigabase at the $100,000 cost benchmark, making it the first company to announce the achievement of the first goal.
NHGRI wants the advantage of next-generation sequencing tools for its comparative genomics projects. The Cancer Genome Project, under the auspices of the National Institutes of Health, also suggests a nearly bottomless market for affordable gene sequencing. More speculatively, an affordable genome could make the dream of personalized medicine a reality, by enabling the sequencing of an individual's genome at a cost low enough to allow the information to become a routine part of one's medical record.
One of the newest winners of NHGRI's $100,000 genome grant, Intelligent Bio-Systems, of Waltham, Massachusetts, is developing a four-color sequencing-by-synthesis method using cleavable fluorescent nucleotide reversible terminators—an approach similar to that of Solexa. It is placing instruments in selected laboratories for beta testing, with a technology that features faster run cycles, less up-front expense and less costly implementation. "We're trying to design the system so that when the market is ready, it could actually be placed into a clinical laboratory," says CEO Steven Gordon. "The instrument cost is low enough that it could be used for clinical tests."
Companies have also started to win bids under the NHGRI $1,000 genome program. Unlike the $100,000 technologies, which focus on refining and improving existing methods, the conception of a $1,000 genome requires an entirely different paradigm—a discontinuous innovation. Helicos' technology, unlike the cluster-based approaches of 454, Agencourt and Solexa, could provide such a leap: in the first commercial award under the $1,000 program, it received, in October 2006, a $2 million grant to further develop its single-molecule approach.
According to Steve Lombardi, senior vice president of Marketing at Helicos, "If you had perfect chemistry, and each step was 99.99%, the instrument would generate 100 billion bases a day. The instrument is being designed for that throughput, but the first-generation chemistry will have a smaller yield—around 600 megabases per day." Improvements in chemistry could move Helicos to the $1,000 genome "in the first few years," he claims—well ahead of the NHGRI goal of 2014.
Over its three-year history, Helicos has raised $67 million in venture funding; the figure for Solexa was well over $100 million. Venture capitalists' appetite for these technologies is still strong. In December 2006, Pacific Biosciences of Menlo Park, California, raised $50 million in venture capital to further develop its single-molecule detection system, first published in 2003 (Science 299, 682–686, 2003). Others are combining forces to gain the resources and technology breadth to compete. Also in December, NABsys, Inc. of Providence, Rhode Island, which has a $1,000 genome technology with a three-year delivery goal, according to CEO Barret Bready, acquired GeneSpectrum, merging its nanopore technology with GeneSpectrum's DNA hybridization technology to create hybridization-assisted nanopore sequencing.
Published online: 1 February 2007.
2007-02-28
What is junk DNA, and what is it worth
from Science American ASK THE EXPERTS: BIOLOGY
What is junk DNA, and what is it worth?
A. Khajavinia
Wojciech Makalowski, a Pennsylvania State University biology professor and researcher in computational evolutionary genomics, answers this query.
Our genetic blueprint consists of 3.42 billion nucleotides packaged in 23 pairs of linear chromosomes. Most mammalian genomes are of comparable size—the mouse script is 3.45 billion nucleotides, the rat's is 2.90 billion, the cow's is 3.65 billion—and code for a similar number of genes: about 35,000. Of course, extremes exist: the bent-winged bat (Miniopterus schreibersi) has a relatively small 1.69-billion-nucleotide genome; the red viscacha rat (Tympanoctomys barrerae) has a genome that is 8.21 billion nucleotides long. Among vertebrates, the highest variability in genome size exists in fish: the green puffer fish (Chelonodon fluviatilis) genome contains only 0.34 billion nucleotides, while the marbled lungfish (Protopterus aethiopicus) genome is gigantic, with almost 130 billion. Interestingly, all animals have a large excess of DNA that does not code for the proteins used to build bodies and catalyze chemical reactions within cells. In humans, for example, only about 2 percent of DNA actually codes for proteins.
For decades, scientists were puzzled by this phenomenon. With no obvious function, the noncoding portion of a genome was declared useless or sometimes called "selfish DNA," existing only for itself without contributing to an organism's fitness. In 1972 the late geneticist Susumu Ohno coined the term "junk DNA" to describe all noncoding sections of a genome, most of which consist of repeated segments scattered randomly throughout the genome.
Typically these sections of junk DNA come about through transposition, or movement of sections of DNA to different positions in the genome. As a result, most of these regions contain multiple copies of transposons, which are sequences that literally copy or cut themselves out of one part of the genome and reinsert themselves somewhere else.
Elements that use copying mechanisms to move around the genome increase the amount of genetic material. In the case of "cut and paste" elements, the process is slower and more complicated, and involves DNA repair machinery. Nevertheless, if transposon activity happens in cells that give rise to either eggs or sperm, these genes have a good chance of integrating into a population and increasing the size of the host genome.
Although very catchy, the term "junk DNA" repelled mainstream researchers from studying noncoding genetic material for many years. After all, who would like to dig through genomic garbage? Thankfully, though, there are some clochards who, at the risk of being ridiculed, explore unpopular territories. And it is because of them that in the early 1990s, the view of junk DNA, especially repetitive elements, began to change. In fact, more and more biologists now regard repetitive elements as genomic treasures. It appears that these transposable elements are not useless DNA. Instead, they interact with the surrounding genomic environment and increase the ability of the organism to evolve by serving as hot spots for genetic recombination and by providing new and important signals for regulating gene expression.
Genomes are dynamic entities: new functional elements appear and old ones become extinct. And so, junk DNA can evolve into functional DNA. The late evolutionary biologist Stephen Jay Gould and paleontologist Elisabeth Vrba, now at Yale University, employed the term "exaptation" to explain how different genomic entities may take on new roles regardless of their original function—even if they originally served no purpose at all. With the wealth of genomic sequence information at our disposal, we are slowly uncovering the importance of non-protein-coding DNA.
In fact, new genomic elements are being discovered even in the human genome, five years after the deciphering of the full sequence. Last summer developmental biologist Gill Bejerano, then a postdoctoral fellow at the University of California, Santa Cruz, and now a professor at Stanford University, and his colleagues discovered that during vertebrate evolution, a novel retroposon—a DNA fragment, reverse-transcribed from RNA, that can insert itself anywhere in the genome—was exapted as an enhancer, a signal that increases a gene's transcription. On the other hand, anonymous sequences that are nonfunctional in one species may, in another organism, become an exon—a section of DNA that is eventually transcribed to messenger RNA. Izabela Makalowska of Pennsylvania State University recently showed that this mechanism quite often leads to another interesting feature in the vertebrate genomes, namely overlapping genes—that is, genes that share some of their nucleotides.
These and countless other examples demonstrate that repetitive elements are hardly "junk" but rather are important, integral components of eukaryotic genomes. Risking the personification of biological processes, we can say that evolution is too wise to waste this valuable information.
What is junk DNA, and what is it worth?
A. Khajavinia
Wojciech Makalowski, a Pennsylvania State University biology professor and researcher in computational evolutionary genomics, answers this query.
Our genetic blueprint consists of 3.42 billion nucleotides packaged in 23 pairs of linear chromosomes. Most mammalian genomes are of comparable size—the mouse script is 3.45 billion nucleotides, the rat's is 2.90 billion, the cow's is 3.65 billion—and code for a similar number of genes: about 35,000. Of course, extremes exist: the bent-winged bat (Miniopterus schreibersi) has a relatively small 1.69-billion-nucleotide genome; the red viscacha rat (Tympanoctomys barrerae) has a genome that is 8.21 billion nucleotides long. Among vertebrates, the highest variability in genome size exists in fish: the green puffer fish (Chelonodon fluviatilis) genome contains only 0.34 billion nucleotides, while the marbled lungfish (Protopterus aethiopicus) genome is gigantic, with almost 130 billion. Interestingly, all animals have a large excess of DNA that does not code for the proteins used to build bodies and catalyze chemical reactions within cells. In humans, for example, only about 2 percent of DNA actually codes for proteins.
For decades, scientists were puzzled by this phenomenon. With no obvious function, the noncoding portion of a genome was declared useless or sometimes called "selfish DNA," existing only for itself without contributing to an organism's fitness. In 1972 the late geneticist Susumu Ohno coined the term "junk DNA" to describe all noncoding sections of a genome, most of which consist of repeated segments scattered randomly throughout the genome.
Typically these sections of junk DNA come about through transposition, or movement of sections of DNA to different positions in the genome. As a result, most of these regions contain multiple copies of transposons, which are sequences that literally copy or cut themselves out of one part of the genome and reinsert themselves somewhere else.
Elements that use copying mechanisms to move around the genome increase the amount of genetic material. In the case of "cut and paste" elements, the process is slower and more complicated, and involves DNA repair machinery. Nevertheless, if transposon activity happens in cells that give rise to either eggs or sperm, these genes have a good chance of integrating into a population and increasing the size of the host genome.
Although very catchy, the term "junk DNA" repelled mainstream researchers from studying noncoding genetic material for many years. After all, who would like to dig through genomic garbage? Thankfully, though, there are some clochards who, at the risk of being ridiculed, explore unpopular territories. And it is because of them that in the early 1990s, the view of junk DNA, especially repetitive elements, began to change. In fact, more and more biologists now regard repetitive elements as genomic treasures. It appears that these transposable elements are not useless DNA. Instead, they interact with the surrounding genomic environment and increase the ability of the organism to evolve by serving as hot spots for genetic recombination and by providing new and important signals for regulating gene expression.
Genomes are dynamic entities: new functional elements appear and old ones become extinct. And so, junk DNA can evolve into functional DNA. The late evolutionary biologist Stephen Jay Gould and paleontologist Elisabeth Vrba, now at Yale University, employed the term "exaptation" to explain how different genomic entities may take on new roles regardless of their original function—even if they originally served no purpose at all. With the wealth of genomic sequence information at our disposal, we are slowly uncovering the importance of non-protein-coding DNA.
In fact, new genomic elements are being discovered even in the human genome, five years after the deciphering of the full sequence. Last summer developmental biologist Gill Bejerano, then a postdoctoral fellow at the University of California, Santa Cruz, and now a professor at Stanford University, and his colleagues discovered that during vertebrate evolution, a novel retroposon—a DNA fragment, reverse-transcribed from RNA, that can insert itself anywhere in the genome—was exapted as an enhancer, a signal that increases a gene's transcription. On the other hand, anonymous sequences that are nonfunctional in one species may, in another organism, become an exon—a section of DNA that is eventually transcribed to messenger RNA. Izabela Makalowska of Pennsylvania State University recently showed that this mechanism quite often leads to another interesting feature in the vertebrate genomes, namely overlapping genes—that is, genes that share some of their nucleotides.
These and countless other examples demonstrate that repetitive elements are hardly "junk" but rather are important, integral components of eukaryotic genomes. Risking the personification of biological processes, we can say that evolution is too wise to waste this valuable information.
How to Deal with False Research Findings
February 27, 2007
The Science of Getting It Wrong: How to Deal with False Research Findings
The key may be for researchers to work closer and check one another's results
By JR Minkel
FALSE POSITIVES: Researchers poring over their samples for novel results may be contributing to a flood of false research results. Tighter collaboration between investigators may be one way to reduce such errors.
Talk about making waves. Two years ago medical researcher John Ioannidis of the University of Ioannina in Greece offered mathematical "proof" that most published research results are wrong. Now, statisticians using similar methods found—not surprisingly—that the more researchers reproduce a finding, the better chance it has of being true.
Another research team says researchers have to draw conclusions from imperfect information, but offers a way to draw the line between justified and unjustified risks.
Meantime, in a possible sign of change, some genetics researchers have begun working more closely in an effort to prevent errors and enhance the accuracy of their results.
In his widely read 2005 PLoS Medicine paper, Ioannidis, a clinical and molecular epidemiologist, attempted to explain why medical researchers must frequently repeal past claims. In the past few years alone, researchers have had to backtrack on the health benefits of low-fat, high-fiber diets and the value and safety of hormone replacement therapy as well as the arthritis drug Vioxx, which was pulled from the market after being found to cause heart attacks and strokes in high-risk patients.
Using simple statistics, without data about published research, Ioannidis argued that the results of large, randomized clinical trials—the gold standard of human research—were likely to be wrong 15 percent of the time and smaller, less rigorous studies are likely to fare even worse.
Among the most likely reasons for mistakes, he says: a lack of coordination by researchers and biases such as tending to only publish results that mesh with what they expected or hoped to find. Interestingly, Ioannidis predicted that more researchers in the field are not necessarily better—especially if they are overly competitive and furtive, like the fractured U.S. intelligence community, which failed to share information that might have prevented the September 11, 2001, terrorist strikes on the World Trade Center and the Pentagon.
But Ioannidis left out one twist: The odds that a finding is correct increase every time new research replicates the same result, according to a study published in the current PLoS Medicine. Lead study author Ramal Moonesinghe, a statistician at the Centers for Disease Control and Prevention, says that for simplicity's sake his group ignored the possibility that results can be replicated by repeating the same biases. The presence of bias reduces but does not erase the value of replication, he says.
"I fully agree that replication is key for improving credibility & replication is more important than discovery," Ioannidis says. But he adds that biases also have to be weeded out, otherwise replication may not be enough. For example, researchers reported in a much touted 2006 Science article that they had discovered a gene variant that seemed to confer a risk for obesity, and they replicated the results in four human populations. Last month, they acknowledged that the finding was probably wrong.
Ioannidis says that researchers have become increasingly sophisticated at acquiring large amounts of data from genomics and other studies, and at spinning it in different ways—much like TV weathercasters proclaiming every day a record-setting meteorological event of some sort. As a result, he says, it is easy to come up with findings that are "significant" in the statistical sense, yet not scientifically valid.
To deal with this poverty of riches, Ioannidis proposes that researchers cooperate more to confirm one another's findings Toward that end, he and other genetics researchers two years ago established a network of research consortia now consisting of 26 groups, he says, each with a dozen to hundreds of members, for investigators studying various cancers, HIV, Parkinson's disease and other disorders. The groups are intended to help teams in each field replicate one another's work.
Networks or not, doctors and health officials also have to decide how to treat patients based on published research that could be overturned, notes oncologist Benjamin Djulbegovic of the H. Lee Moffitt Cancer Center and Research Institute in Tampa. He and his colleagues contend in a second PLoS paper that physicians' decisions should be based on a mix of estimates of error for different types of studies (such as those that Ioannidis calculated), the potential benefits of the treatments reported in those studies, and how much of those benefits their patients can do without (or how much harm they can live with) if the finding turns out to be false.
"We can't work with 100 percent certainty," Djulbegovic says. "The question is: How false is false?" A well conducted randomized trial is more likely to produce correct results, but a less rigorous study might still satisfy a physician if the risks are low and its potential benefits are great, he says.
Ioannidis agrees that perfect certainty is impossible. "If you have a severe disease and there is only one medication available, and you know that it is only 5 percent likely to work, why not use it?" he says. But implementing such a calculus is trickier than it appears, he adds, because "we cannot assume that an intervention is necessarily safe in the absence of strong data testifying to this."
from Scientific American
The Science of Getting It Wrong: How to Deal with False Research Findings
The key may be for researchers to work closer and check one another's results
By JR Minkel
FALSE POSITIVES: Researchers poring over their samples for novel results may be contributing to a flood of false research results. Tighter collaboration between investigators may be one way to reduce such errors.
Talk about making waves. Two years ago medical researcher John Ioannidis of the University of Ioannina in Greece offered mathematical "proof" that most published research results are wrong. Now, statisticians using similar methods found—not surprisingly—that the more researchers reproduce a finding, the better chance it has of being true.
Another research team says researchers have to draw conclusions from imperfect information, but offers a way to draw the line between justified and unjustified risks.
Meantime, in a possible sign of change, some genetics researchers have begun working more closely in an effort to prevent errors and enhance the accuracy of their results.
In his widely read 2005 PLoS Medicine paper, Ioannidis, a clinical and molecular epidemiologist, attempted to explain why medical researchers must frequently repeal past claims. In the past few years alone, researchers have had to backtrack on the health benefits of low-fat, high-fiber diets and the value and safety of hormone replacement therapy as well as the arthritis drug Vioxx, which was pulled from the market after being found to cause heart attacks and strokes in high-risk patients.
Using simple statistics, without data about published research, Ioannidis argued that the results of large, randomized clinical trials—the gold standard of human research—were likely to be wrong 15 percent of the time and smaller, less rigorous studies are likely to fare even worse.
Among the most likely reasons for mistakes, he says: a lack of coordination by researchers and biases such as tending to only publish results that mesh with what they expected or hoped to find. Interestingly, Ioannidis predicted that more researchers in the field are not necessarily better—especially if they are overly competitive and furtive, like the fractured U.S. intelligence community, which failed to share information that might have prevented the September 11, 2001, terrorist strikes on the World Trade Center and the Pentagon.
But Ioannidis left out one twist: The odds that a finding is correct increase every time new research replicates the same result, according to a study published in the current PLoS Medicine. Lead study author Ramal Moonesinghe, a statistician at the Centers for Disease Control and Prevention, says that for simplicity's sake his group ignored the possibility that results can be replicated by repeating the same biases. The presence of bias reduces but does not erase the value of replication, he says.
"I fully agree that replication is key for improving credibility & replication is more important than discovery," Ioannidis says. But he adds that biases also have to be weeded out, otherwise replication may not be enough. For example, researchers reported in a much touted 2006 Science article that they had discovered a gene variant that seemed to confer a risk for obesity, and they replicated the results in four human populations. Last month, they acknowledged that the finding was probably wrong.
Ioannidis says that researchers have become increasingly sophisticated at acquiring large amounts of data from genomics and other studies, and at spinning it in different ways—much like TV weathercasters proclaiming every day a record-setting meteorological event of some sort. As a result, he says, it is easy to come up with findings that are "significant" in the statistical sense, yet not scientifically valid.
To deal with this poverty of riches, Ioannidis proposes that researchers cooperate more to confirm one another's findings Toward that end, he and other genetics researchers two years ago established a network of research consortia now consisting of 26 groups, he says, each with a dozen to hundreds of members, for investigators studying various cancers, HIV, Parkinson's disease and other disorders. The groups are intended to help teams in each field replicate one another's work.
Networks or not, doctors and health officials also have to decide how to treat patients based on published research that could be overturned, notes oncologist Benjamin Djulbegovic of the H. Lee Moffitt Cancer Center and Research Institute in Tampa. He and his colleagues contend in a second PLoS paper that physicians' decisions should be based on a mix of estimates of error for different types of studies (such as those that Ioannidis calculated), the potential benefits of the treatments reported in those studies, and how much of those benefits their patients can do without (or how much harm they can live with) if the finding turns out to be false.
"We can't work with 100 percent certainty," Djulbegovic says. "The question is: How false is false?" A well conducted randomized trial is more likely to produce correct results, but a less rigorous study might still satisfy a physician if the risks are low and its potential benefits are great, he says.
Ioannidis agrees that perfect certainty is impossible. "If you have a severe disease and there is only one medication available, and you know that it is only 5 percent likely to work, why not use it?" he says. But implementing such a calculus is trickier than it appears, he adds, because "we cannot assume that an intervention is necessarily safe in the absence of strong data testifying to this."
from Scientific American
2007-02-13
on Spring Festival holiday！
on February 12th, 1809, a great man was born——CHARLES ROBERT DARWIN（February 12, 1809 to April 19, 1882）, So let's take a moment to ponder the central dogma of modern biology, best embodied by the title of a 1973 essay by evolutionary biologist (and Russian Orthodox Christian) Theodosius Dobzhansky:
Nothing in Biology Makes Sense Except in the Light of Evolution.
Nothing in Biology Makes Sense Except in the Light of Evolution.
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