Pigs are valuable large animal models for biomedical and genetic research, but insights into the tissue- and cell-type-specific transcriptome and heterogeneity remain limited. By leveraging single-cell RNA sequencing, scientists now generated a multiple-organ single-cell transcriptomic map containing over 200,000 pig cells from 20 tissues/organs. They were able to comprehensively characterize the heterogeneity of cells in tissues and to identify 234 cell clusters, representing 58 major cell types. In-depth integrative analysis of endothelial cells reveals a high degree of heterogeneity. They also identified several functionally distinct endothelial cell phenotypes, including an endothelial to mesenchymal transition subtype in adipose tissues. Intercellular communication analysis predicts tissue- and cell type-specific crosstalk between endothelial cells and other cell types through the VEGF, PDGF, TGF-β, and BMP pathways. Regulon analysis of single-cell transcriptome of microglia in pig and 12 other species further identifies MEF2C as an evolutionally conserved regulon in the microglia.
This important work describes the landscape of single-cell transcriptomes within diverse pig organs and identifies the heterogeneity of endothelial cells and evolutionally conserved regulon in microglia.
Just two years ago, GEDmatch was still an obscure genealogy website, known only to a million or so hobbyist DNA sleuths looking to fill in their family trees. The site was free, public, and run by two guys with a knack for writing algorithms that helped relatives find each other. All in all, it was a pretty controversy-free place.
That all changed in April of 2018, when news broke that police had used GEDmatch to identify a suspect in the 40-year-old Golden State Killer case. As the site emerged as a crime-fighting tool, some users and privacy experts began to worry about how people’s genetic data might ensnare them in criminal investigations, when all they wanted was to learn about their family history. The transition has been rocky for GEDmatch. One drama after another has engulfed the website: Police searches have grown increasingly invasive; the site’s owners tried to react with changes to its terms of service that ended up backfiring; and white-hat hackers pointed out glaring security flaws. But starting Monday, that’s all someone else’s problem.
Early December 2019, GEDmatch announced it was being taken over by a new owner, the forensic genomics firm Verogen. The San Diego-based company spun out of sequencing giant Illumina two years ago, specializing in next-generation DNA testing services catered to law enforcement. With the acquisition of GEDmatch, Verogen may also start offering genealogy searches like the ones that have so far identified suspects in as many as 70 cases. “Never before have we as a society had the opportunity to serve as a molecular eyewitness, enabling law enforcement to solve violent crimes efficiently and with certainty,” Verogen CEO Brett Williams said in a statement announcing the deal. The terms of the agreement were not disclosed.
Reactions so far, have been mixed. “I suspect this will be the last straw for all the genealogists who don’t want to share with law enforcement,” Debbie Kennett, a genealogist and honorary research associate at University College London, told WIRED. On Monday GEDmatch updated its terms of service to reflect the new ownership, but it did not alert users via email. Kennett found out from a Facebook group discussion. When she tried to log into GEDmatch, she discovered she was locked out until she accepted the new terms. Additional options included deciding later and permanently deleting all her data from the GEDmatch servers.
According to a Verogen spokesperson, whatever settings users had earlier selected for their GEDmatch profiles—opting in or out of police searches—will remain under the new terms. GEDmatch itself has not always stuck to its word on such matters. Earlier this month, the site’s users discovered their privacy settings weren’t ironclad, when reports surfaced that a Florida detective had obtained a warrant to search the site’s full database, including individuals who had opted out of cooperating with law enforcement. A few days prior, a team of genetic security researchers revealed a flaw in GEDmatch’s relative-matching algorithm that would allow a hacker to scrape more than 90 percent of users’ DNA data. Verogen’s Williams says GEDmatch has already addressed these security issues, and that his company will continue to monitor other possible vulnerabilities.
AI tools could help us turn information gleaned from genetic sequencing into life-saving therapies. Almost 15 years after scientists first sequenced the human genome, making sense of the enormous amount of data that encodes human life remains a formidable challenge. But it is also precisely the sort of problem that machine learning excels at.
Google has now released a tool called DeepVariant that uses the latest AI techniques to build a more accurate picture of a person’s genome from sequencing data. DeepVariant helps turn high-throughput sequencing readouts into a picture of a full genome. It automatically identifies small insertion and deletion mutations and single-base-pair mutations in sequencing data.
High-throughput sequencing became widely available in the 2000s and has made genome sequencing more accessible. But the data produced using such systems has offered only a limited, error-prone snapshot of a full genome. It is typically challenging for scientists to distinguish small mutations from random errors generated during the sequencing process, especially in repetitive portions of a genome. These mutations may be directly relevant to diseases such as cancer.
A number of tools exist for interpreting these readouts, including GATK, VarDict, and FreeBayes. However, these software programs typically use simpler statistical and machine-learning approaches to identifying mutations by attempting to rule out read errors. “One of the challenges is in difficult parts of the genome, where each of the tools has strengths and weaknesses,” says Brad Chapman, a research scientist at Harvard’s School of Public Health who tested an early version of DeepVariant. “These difficult regions are increasingly important for clinical sequencing, and it’s important to have multiple methods.”
DeepVariant was developed by researchers from the Google Brain team, a group that focuses on developing and applying AI techniques, and Verily, another Alphabet subsidiary that is focused on the life sciences. The team collected millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle (GIAB) project, a public-private effort to promote genomic sequencing tools and techniques. They fed the data to a deep-learning system and painstakingly tweaked the parameters of the model until it learned to interpret sequenced data with a high level of accuracy.
Last year, DeepVariant won first place in the PrecisionFDA Truth Challenge, a contest run by the FDA to promote more accurate genetic sequencing. “The success of DeepVariant is important because it demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems,” says Brendan Frey, CEO of Deep Genomics.
The release of DeepVariant is the latest sign that machine learning may be poised to boost progress in genomics. Deep Genomics is one of several companies trying to use AI approaches such as deep learning to tease out genetic causes of diseases and to identify potential drug therapies (see “An AI-Driven Genomics Company Is Turning to Drugs”).
Deep Genomics aims to develop drugs by using deep learning to find patterns in genomic and medical data. Frey says AI will eventually go well beyond helping to sequence genomic data. “The gap that is currently blocking medicine right now is in our inability to accurately map genetic variants to disease mechanisms and to use that knowledge to rapidly identify life-saving therapies,” he says.
Another prominent company in this area is Wuxi Nextcode, which has offices in Shanghai, Reykjavik, and Cambridge, Massachusetts. Wuxi Nextcode has amassed the world’s largest collection of fully sequenced human genomes, and the company is investing heavily in machine-learning methods.
DeepVariant will also be available on the Google Cloud Platform. Google and its competitors are furiously adding machine-learning features to their cloud platforms in an effort to lure anyone who might want to tap into the latest AI techniques (see “Ambient AI Is About to Devour the Software Industry”).
In general, AI figures to help many aspects of medicine take big leaps forward in the coming years. There are opportunities to mine many different kinds of medical data—from images or medical records, for example— to predict ailments that a human doctor might miss (see “The Machines Are Getting Ready to Play Doctor” and “A New Algorithm for Palliative Care”).
Sequencing messenger RNA molecules from individual cells offers a glimpse into the lives of those cells, revealing what they’re doing at a particular time. However, the equipment required to do this kind of analysis is cumbersome and not widely available.
MIT researchers have now developed a portable technology that can rapidly prepare the RNA of many cells for sequencing simultaneously, which they believe will enable more widespread use of this approach. The new technology, known as Seq-Well, could allow scientists to more easily identify different cell types found in tissue samples, helping them to study how immune cells fight infection and how cancer cells respond to treatment, among other applications.
“Rather than trying to pick one marker that defines a cell type, using single-cell RNA sequencing we can go in and look at everything a cell is expressing at a given moment. By finding common patterns across cells, we can figure out who those cells are,” says Alex K. Shalek, the Hermann L.F. von Helmholtz Career Development Assistant Professor of Health Sciences and Technology, an assistant professor of chemistry, and a member of MIT’s Institute for Medical Engineering and Science.
Shalek and his colleagues have spent the past several years developing single-cell RNA sequencing strategies. In the new study, he teamed up with J. Christopher Love, an associate professor of chemical engineering at MIT’s Koch Institute for Integrative Cancer Research, to create a new version of the technology that can rapidly analyze large numbers of cells, with very simple equipment.
“We’ve combined his technologies with some of ours in a way that makes it really accessible for researchers who want to do this type of sequencing on a range of different clinical samples and settings,” Love says. “It overcomes some of the barriers that are facing the adoption of these techniques more broadly.”
Love and Shalek are the senior authors of a paper describing the new technique in the Feb. 13 issue of Nature Methods. The paper’s lead authors are Research Associate Todd Gierahn and graduate students Marc H. Wadsworth II and Travis K. Hughes.
Most antibiotics in use today are based on natural molecules produced by bacteria—and given the rise of antibiotic resistance, there’s an urgent need to find more of them. Yet coaxing bacteria to produce new antibiotics is a tricky proposition. Most bacteria won’t grow in the lab. And even when they do, most of the genes that cause them to churn out molecules with antibiotic properties never get switched on.
The researchers placed tiny droplets of 25 newly discovered antibiotics on a carpet of beta-lactam resistant S. aureus. They identified two compounds that generated circles of dead bacteria (dark spots) around each droplet.
Researchers at The Rockefeller University have found a way around these problems, however. By using computational methods to identify which genes in a microbe’s genome ought to produce antibiotic compounds and then synthesizing those compounds themselves, they were able to discover two promising new antibiotics without having to culture a single bacterium.
The team, which was led by Sean Brady, head of the Laboratory of Genetically Encoded Small Molecules, began by trawling publicly available databases for the genomes of bacteria that reside in the human body. They then used specialized computer software to scan hundreds of those genomes for clusters of genes that were likely to produce molecules known as non-ribosomal peptides that form the basis of many antibiotics. They also used the software to predict the chemical structures of the molecules that the gene clusters ought to produce.
The software initially identified 57 potentially useful gene clusters, which the researchers winnowed down to 30. Brady and his colleagues then used a method called solid-phase peptide synthesis to manufacture 25 different chemical compounds.
By testing those compounds against human pathogens, the researchers successfully identified two closely related antibiotics, which they dubbed humimycin A and humimycin B. Both are found in a family of bacteria called Rhodococcus—microbes that had never yielded anything resembling the humimycins when cultured using traditional laboratory techniques.
The humimycins proved especially effective against Staphylococcus and Streptococcus bacteria, which can cause dangerous infections in humans and tend to grow resistant to various antibiotics. Further experiments suggested that the humimycins work by inhibiting an enzyme that bacteria use to build their cell walls—and once that cell-wall building pathway is interrupted, the bacteria die.
Researchers from the National Institute of Standards and Technology (NIST) and collaborators have proposed a design for the first DNA sequencer based on an electronic nanosensor that can detect tiny motions as small as a single atom.
The proposed device—a type of capacitor, which stores electric charge—is a tiny ribbon of molybdenum disulfide suspended over a metal electrode and immersed in water. The ribbon is 15.5 nm long and 4.5 nm wide. Single-stranded DNA, containing a chain of bases (bits of genetic code), is threaded through a hole 2.5 nm wide in the thin ribbon. The ribbon flexes only when a DNA base pairs up with and then separates from a complementary base affixed to the hole. The membrane motion is detected as an electrical signal.
As described in a new paper, the NIST team made numerical simulations and theoretical estimates to show the membrane would be 79 to 86 percent accurate in identifying DNA bases in a single measurement at speeds up to about 70 million bases per second. Integrated circuits would detect and measure electrical signals and identify bases. The results suggest such a device could be a fast, accurate and cost-effective DNA sequencer, according to the paper.
Conventional sequencing, developed in the 1970s, involves separating, copying, labeling and reassembling pieces of DNA to read the genetic information. Newer methods include automated sequencing of many DNA fragments at once—still costly—and novel "nanopore sequencing" concepts. For example, the same NIST group recently demonstrated the idea of sequencing DNA by passing it through a graphene nanopore, and measuring how graphene's electronic properties respond to strain.
The latest NIST proposal relies on a thin film of molybdenum disulfide—a stable, layered material that conducts electricity and is often used as a lubricant. Among other advantages, this material does not stick to DNA, which can be a problem with graphene. The NIST team suggests the method might even work without a nanopore—a simpler design—by passing DNA across the edge of the membrane.
"This approach potentially solves the issue with DNA sticking to graphene if inserted improperly, because this approach does not use graphene, period," NIST theorist and lead author Alex Smolyanitsky said. "Another major difference is that instead of relying on the properties of graphene or any particular material used, we read motions electrically in an easier way by forming a capacitor. This makes any electrically conductive membrane suitable for the application."
The discovery power of the gene chip is coming to nanotechnology, as a Northwestern University research team develops a tool to rapidly test millions — and perhaps even billions — of different nanoparticles at one time to zero in on the best nanoparticle for a specific use.
When materials are miniaturized, their properties — optical, structural, electrical, mechanical and chemical — change, offering new possibilities. But determining what nanoparticle size and composition are best for a given application, such as catalysts, biodiagnostic labels, pharmaceuticals and electronic devices, is a daunting task.
“As scientists, we’ve only just begun to investigate what materials can be made on the nanoscale,” said Northwestern’s Chad A. Mirkin, a world leader in nanotechnology research and its application, who led the study. “Screening a million potentially useful nanoparticles, for example, could take several lifetimes. Once optimized, our tool will enable researchers to pick the winner much faster than conventional methods. We have the ultimate discovery tool.”
Combinatorial libraries of nanoparticles - more than half never existed on Earth.
Using a Northwestern technique that deposits materials on a surface, Mirkin and his team figured out how to make combinatorial libraries of nanoparticles in a controlled way. (A combinatorial library is a collection of systematically varied structures encoded at specific sites on a surface.) Their study was published today (June 24) by the journal Science.
The nanoparticle libraries are much like a gene chip, Mirkin says, where thousands of different spots of DNA are used to identify the presence of a disease or toxin. Thousands of reactions can be done simultaneously, providing results in just a few hours. Similarly, Mirkin and his team’s libraries will enable scientists to rapidly make and screen millions to billions of nanoparticles of different compositions and sizes for desirable physical and chemical properties.
“The ability to make libraries of nanoparticles will open a new field of nanocombinatorics, where size — on a scale that matters — and composition become tunable parameters,” Mirkin said. “This is a powerful approach to discovery science.”
Using just five metallic elements — gold, silver, cobalt, copper and nickel — Mirkin and his team developed an array of unique structures by varying every elemental combination. In previous work, the researchers had shown that particle diameter also can be varied deliberately on the 1- to 100-nanometer length scale.
QuantuMDx Group is one of the most exciting biotechs to emerge from the UK and is developing a low cost, simple-to-use, handheld laboratory for 15-minute diagnosis of disease at the patient's side, for commercialisation in 2015. The robust device, which reads and sequences DNA and converts it into binary code using a tiny computer chip, is ideally suited to help address the humanitarian health burden by offering molecular diagnostics at a fraction of the price of traditional testing.
Rapidly & accurately detecting and monitoring emerging drug resistance of infectious diseases such as malaria, TB and HIV will enable health professionals to immediately prescribe the most effective drug against that disease. Once the device has passed regulatory approval, it will be available in developed countries for infectious disease testing and rapid cancer profiling and, in time, be available over-the-counter at pharmacies.
Twist Bioscience dramatically scaled down the equipment for synthesizing DNA in a lab, making the process cheaper and faster. The stamp-sized wafers contain 100 microwells. Each of these contains 100 nanowells in which DNA can be synthesized.
AT TWIST BIOSCIENCE’S office in San Francisco, CEO Emily Leproust pulled out of her tote bag two things she carries around everywhere: a standard 96-well plastic plate ubiquitous in biology labs and her company’s invention, a silicon wafer studded with a similar number of nanowells.
Twist’s pitch is that it has dramatically scaled down the equipment for synthesizing DNA in a lab, making the process cheaper and faster. As Leproust gave her spiel, I looked from the jankety plastic plate, the size of two decks of cards side by side, to the sleek stamp-sized silicon wafer and politely nodded along. Then she handed me a magnifying lens to look down the wafer’s nanowells. Inside each nanowell was another 100 microscope holes.
That’s when I actually got it. The 96-well plate was not equivalent to the wafer, the entire plate was equivalent toone nanowell on the wafer. To put a number on it, traditional DNA synthesis machines can make one gene per 96-well plate; Twist’s machine can make 10,000 genes on a silicon wafer set the same size as the plate.
But who wants to order 10,000 genes? Until recently, that question might have been met with silence. “It was a lonely time,” says Leproust of her early fundraising efforts for Twist. Fast forward a couple years, though, and Twist has just signed a deal to sell at least 100 million letters of DNA—equivalent to tens of thousands of genes—to Ginkgo Bioworks, a synthetic biology outfit that inserts genes into yeast to make scents like rose oil or flavors like vanillin. Ginkgo is at the forefront of a wave of synthetic biology companies, bolstered by new gene-editing technologies like Crispr and investor interest.
“We’re Intel and Ginkgo is Microsoft,” says Leproust, which sounds exactly the kind of rhetoric you hear all the time in startupland. But her words reveal Twist’s specific ambition to be the driver behind synthetic biology innovations. Synthesizing genes in a lab allows biologists to design—down to the letter—the ones they want to test. Companies out there are already tinkering with DNA in various cells to create spider silk, cancer treatments, biodegradable plastic, diesel fuel—and Twist’s founders thinks the company can become the driving technology behind that new world.
Researchers from the University of Washington’s Departments of Physics and Genome Sciences have developed a nanopore sequencing technique reaching read lengths of several thousand bases. The result is the latest in a series of advances in nanopore technology developed at the university.
“This is the first time anyone has shown that nanopores can be used to generate interpretable signatures corresponding to very long DNA sequences from real-world genomes,” said co-author Jay Shendure, an associate professor in Genome Sciences, “It’s a major step forward.
”The idea for nanopore sequencing originated in the 90s: a lipid membrane, similar to the material that makes up the cell wall, acts as a barrier separating two liquids. Inserted into the membrane is a tiny gap, just nanometers across, called a nanopore. By applying a voltage difference across the barrier, ions in the liquid try to move between the two sides of the barrier and the only way to do this is to flow through the nanopore. The movement of the charged molecules between the two liquids is a current, just like electrons moving along a wire in an electrical circuit, and can be recorded.
Any DNA in the system is also pulled towards the other side of the barrier by the voltage difference, since DNA is negatively charged, and just like the ions it has to pass through the nanopore. The difference is that the DNA is much bigger than the ions and partially blocks the nanopore, making it harder for the smaller molecules to pass through. As the ions are blocked by the DNA, there is a measurable difference in the current flowing across the membrane which is dependent on the DNA base passing through the nanopore. By measuring the changing current, information can be gained on the bases passing through.
The researchers created the nanopore by inserting a single protein called Mycobacterium smegmatis porin A, or MspA, in the membrane. MspA is normally found lining the membrane of a species of bacteria, controlling the intake of nutrients.
One challenge the researchers faced was the control of the DNA passing through the nanopore. Normally, the DNA would zip through the MspA nanopore too fast to detect the changes in the current. The researchers slowed the DNA movement through the pore using a second protein called phi29 DNA polymerase (DNAP), which captures DNA and slows its movement through the pore.
The shape of the protein MspA meant that several bases passed through the nanopore at one time and the current changes were the result of a combination of those bases. This presented another challenge. Since several bases passed through the nanopore at one time, the researchers needed a way to decipher what the current changes meant. To do this, they first made a library of DNA sequences that contains all possible combinations of 4 nucleotides (for the mathematically inclined, the library is 44 = 256 bases long – a string of 4 bases with 4 possible choices for each DNA base). The library, whose sequence was already known, was run though the nanopore first to find the current associated with each set of DNA base combinations. They combined the library measurements with known genome sequences to generate a set of expected current changes that could be compared to experimental measurements.
The researchers tested their approach by sequencing the entire genome of bacteriophage Phi X 174, a virus that infects bacteria and is used as a benchmark for evaluating new sequencing technologies. The impressive feat here is the length of the genome they sequenced – the Phi X 174 genome is 4,500 bases long. Other nanopore technologies have been limited to sequencing DNA fragments that were much shorter.
“Despite the remaining hurdles, our demonstration that a low-cost device can reliably read the sequences of naturally occurring DNA and can interpret DNA segments as long as 4,500 nucleotides in length represents a major advance in nanopore DNA sequencing,” explained Gundlach.
Researchers at the BBSRC-funded Babraham Institute, in collaboration with the Wellcome Trust Sanger Institute Single Cell Genomics Centre, have developed a powerful new single-cell technique to help investigate how the environment affects our development and the traits we inherit from our parents. The technique can be used to map all of the ‘epigenetic marks’ on the DNA within a single cell. This single-cell approach will boost understanding of embryonic development, could enhance clinical applications like cancer therapy and fertility treatments, and has the potential to reduce the number of mice currently needed for this research.
‘Epigenetic marks’ are chemical tags or proteins that mark DNA and act as a kind of cellular memory. They do not change the DNA sequence but record a cell’s experiences onto the DNA, which allows cells to remember an experience long after it has faded. Placing these tags is part of normal development; they tell genes whether to be switched on or off and so can determine how the cell develops. Different sets of active genes make a skin cell different from a brain cell, for example. However, environmental cues such as diet can also alter where epigenetic tags are laid down on DNA and influence an organism’s long-term health.
Dr Gavin Kelsey, from the Babraham Institute, said: “The ability to capture the full map of these epigenetic marks from individual cells will be critical for a full understanding of early embryonic development, cancer progression and aid the development of stem cell therapies.
“Epigenetics research has mostly been reliant on using the mouse as a model organism to study early development. Our new single-cell method gives us an unprecedented ability to study epigenetic processes in human early embryonic development, which has been restricted by the very limited amount of tissue available for analysis.”
The new research, published in Nature Methods, offers a new single-cell technique capable of analysing DNA methylation – one of the key epigenetic marks – across the whole genome. The method treats the cellular DNA with a chemical called bisulphite. Treated DNA is then amplified and read on high-throughput sequencing machines to show up the location of methylation marks and the genes being affected.
These analyses will help to define how epigenetic changes in individual cells during early development drive cell fate. Current methods observe epigenetic marks in multiple, pooled cells. This can obscure modifications taking place in individual cells at a time in development when each cell has the potential to form in a unique way. The new method has already revealed that many of the methylation marks that differ between individual cells are precisely located in sites that control gene activity.
We are now on the verge of a health data breakthrough, in which computers will be able to do similar diagnostic tasks, by analyzing massive amounts of data, including genome sequences, risk factors, medical histories, drug interactions, and more.
Looking at this trend last year, venture capitalist Vinod Khosla made the bold claim that technology will replace 80 percent of companies eventually. The reality is probably more nuanced: Far from threatening to put doctors out of jobs, the falling prices of data analysis and genome sequencing are enabling them with tools they could only dream of even a few years ago.
At the Mount Sinai Hospital in New York, Joel Dudley, Ph.D. uses Ayasdi’s products to discover how patients with certain genes are more likely to develop some diseases (diabetes, cardiovascular conditions…) as well as how genes influence the performance of a treatment, or may reveal risks of later relapses that can be prepared for.
Already 11,000 patients at Mount Sinai have had their genome sequenced, a pool large enough for meaningful analysis, although Ayasdi tells us “those are still early days for the industry. There are no plans to act on that data directly with individual patients just yet.”
Right now the Mount Sinai community is working at organizing itself to make the useful information available to the frontline staff. And another 30,000 patients may soon sign the consent form and opt in to participate in this new way to explore which care is best for them.
The exploration of big data by the enterprise is becoming less of a competitive edge and turning into more of a must-have. Similarly, hospitals may have to adopt genetic analysis as a rule of thumb sooner rather than later. Mount Sinai is unusual today in pioneering regular genetic screenings, but it soon may become commonplace.
Oxford Nanopore Technologies Ltd., the U.K. company developing portable gene sequencers, will begin providing its MinION handheld device to some customers to test, a sign it’s taking steps toward selling the instrument.
Oxford Nanopore's disposable DNA sequencer is about the size of a USB memory stick that can be plugged directly into a laptop or desktop computer and used to perform a single-molecule sensing experiment. The device is expected to sell for around $1,000, according to the company.
Oxford Nanopore Technologies also unveiled a larger benchtop version of the technology. It says a configuration of 20 of the benchtop instruments could completely sequence a human genome in 15 minutes.
The technology is based on a radically different sequencing method that has been in the work for more than a decade at Oxford University, Harvard and the University of California, Santa Cruz. DNA strands are pulled through nanopores embedded in a polymer. As the DNA passes through the nanopore, specific sequences are identified based on varying electronic signals from the different bases. As a result, the technology can read DNA sequences directly and continuously. The company says double-stranded DNA can be sensed directly from blood.
These type of nanopore machines should be suitable to sit on the bench of a small lab, running small projects and with small budgets and floorspace. However, this isn’t the full story. Each individual machine is rocking the VCR-machine-circa-1992 look, and the reason for this becomes clear when you see many of them together. The boxes are designed to fit together in standard computing cluster racks, and Oxford Nanopore refer to each of the individual machines as “nodes”. The nodes connect together via a standard network, and can talk to each other, as well as reporting data in real time through the network to other computers. When joined together like this, one machine can be designated as the control node, and during sequencing many nodes can be assigned to sequence the same sample.
Another aspect is the ability of the machines to react in real time. The sequencer can change aspects of its behavior in response to orders given during sequencing. Some of these will be automatic quality-control changes; the salt concentration and the temperature can change to optimize the sequence speed or quality. The machines can also be given basic preset targets; sequence until we have enough reads, or enough coverage, or a good enough idea of the concentration of a particular protein. This means that instead of running the machine for a set period of time, you can instead run until you have what you want.
Also, the machines can be loaded with up to 96 different samples, so you can decide to sequence one sample until you have enough DNA from it, then move onto another one, and so on. The machines can also talk to each other; for instance, four machines could sequence the same sample, and stop once they had produced enough sequence between them. Finally, the machines has built in APIs to allow them to respond to external programs of arbitrary complexity; for instance, you could connect your machines to a computing cluster that is aligning reads and making variant calls as the sequence runs, and you could decide which sample to sequence next based on the SNP calls from the first.
This new generation of sequencing machines is going to raise a whole new set of bioinformatics challanges, as well as requiring scientists to think about experimental design more carefully to make the most of this technology.
While digital technology is extremely good at solving certain problems, it often struggles with tasks that the human brain excels at. In a new study, scientists have leveraged brain-inspired connectivity between artificial neurons to solve a real-world problem of identifying mutations of a new viral species.
In the September issue of the journal Nature, scientists from Texas A&M University, Hewlett Packard Labs and Stanford University have described a new nanodevice that acts almost identically to a brain cell. Furthermore, they have shown that these synthetic brain cells can be joined together to form intricate networks that can then solve problems in a brain-like manner.
"This is the first study where we have been able to emulate a neuron with just a single nanoscale device, which would otherwise need hundreds of transistors," said Dr. R. Stanley Williams, senior author on the study and professor in the Department of Electrical and Computer Engineering. "We have also been able to successfully use networks of our artificial neurons to solve toy versions of a real-world problem that is computationally intense even for the most sophisticated digital technologies."
In particular, the researchers have demonstrated proof of concept that their brain-inspired system can identify possible mutations in a virus, which is highly relevant for ensuring the efficacy of vaccines and medications for strains exhibiting genetic diversity.
Over the past decades, digital technologies have become smaller and faster largely because of the advancements in transistor technology. However, these critical circuit components are fast approaching their limit of how small they can be built, initiating a global effort to find a new type of technology that can supplement, if not replace, transistors.
In addition to this "scaling-down" problem, transistor-based digital technologies have other well-known challenges. For example, they struggle at finding optimal solutions when presented with large sets of data. "Let's take a familiar example of finding the shortest route from your office to your home. If you have to make a single stop, it's a fairly easy problem to solve. But if for some reason you need to make 15 stops in between, you have 43 billion routes to choose from," said Dr. Suhas Kumar, lead author on the study and researcher at Hewlett Packard Labs. "This is now an optimization problem, and current computers are rather inept at solving it."
Kumar added that another arduous task for digital machines is pattern recognition, such as identifying a face as the same regardless of viewpoint or recognizing a familiar voice buried within a din of sounds. But tasks that can send digital machines into a computational tizzy are ones at which the brain excels. In fact, brains are not just quick at recognition and optimization problems, but they also consume far less energy than digital systems. Hence, by mimicking how the brain solves these types of tasks, Williams said brain-inspired or neuromorphic systems could potentially overcome some of the computational hurdles faced by current digital technologies.
To build the fundamental building block of the brain or a neuron, the researchers assembled a synthetic nanoscale device consisting of layers of different inorganic materials, each with a unique function. However, they said the real magic happens in the thin layer made of the compound niobium dioxide.
When a small voltage is applied to this region, its temperature begins to increase. But when the temperature reaches a critical value, niobium dioxide undergoes a quick change in personality, turning from an insulator to a conductor. But as it begins to conduct electric currents, its temperature drops and niobium dioxide switches back to being an insulator.
These back-and-forth transitions enable the synthetic devices to generate a pulse of electrical current that closely resembles the profile of electrical spikes, or action potentials, produced by biological neurons. Further, by changing the voltage across their synthetic neurons, the researchers reproduced a rich range of neuronal behaviors observed in the brain, such as sustained, burst and chaotic firing of electrical spikes. "Capturing the dynamical behavior of neurons is a key goal for brain-inspired computers," said Kumar. "Altogether, we were able to recreate around 15 types of neuronal firing profiles, all using a single electrical component and at much lower energies compared to transistor-based circuits."
Vanderbilt engineers have developed a new method for duplicating DNA that makes devices small enough to hold in your hand that are capable of identifying infectious agents before symptoms appear.
Imagine a “DNA photocopier” small enough to hold in your hand that could identify the bacteria or virus causing an infection even before the symptoms appear. This possibility is raised by a fundamentally new method for controlling a powerful but finicky process called the polymerase chain reaction or PCR.
PCR was developed in 1983 by Kary Mullis, who received the Nobel Prize for his invention. It is generally considered one of the most important advances in the field of molecular biology because it can make billions of identical copies of small segments of DNA so they can be used in molecular and genetic analyses.
Vanderbilt University biomedical engineers Nicholas Adams and Frederick Haselton came up with an out-of-the-box idea, which they call adaptive PCR. It uses left-handed DNA (L-DNA) to monitor and control the molecular reactions that take place in the PCR process.
Left-handed DNA is the mirror image of the DNA found in all living things. It has the same physical properties as regular, right-handed DNA but it does not participate in most biological reactions. As a result, when fluorescently tagged L-DNA is added to a PCR sample, it behaves in an identical way to the regular DNA and provides a fluorescent light signal that reports information about the molecular reactions taking place and can be used to control them.
In order to test their idea, Adams and Haselton recruited Research Assistant Professor of Physics William Gabella to create a working prototype of an adaptive PCR machine and then they tested it extensively with the assistance of biomedical engineering undergraduate Austin Hardcastle.
Will you be among the first to pick your kids’ IQ? As machine learning unlocks predictions from DNA databases, scientists say parents could have choices never before possible.
Nathan Treff was diagnosed with type 1 diabetes at 24. It’s a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too.
So you don’t know who will get it. Treff’s grandfather had it, and lost a leg. But Treff’s three young kids are fine, so far. He’s crossing his fingers they won’t develop it later.
Now Treff, an in vitro fertilization specialist, is working on a radical way to change the odds. Using a combination of computer models and DNA tests, the startup company he’s working with, Genomic Prediction, thinks it has a way of predicting which IVF embryos in a laboratory dish would be most likely to develop type 1 diabetes or other complex diseases. Armed with such statistical scorecards, doctors and parents could huddle and choose to avoid embryos with failing grades.
IVF clinics already test the DNA of embryos to spot rare diseases, like cystic fibrosis, caused by defects in a single gene. But these “preimplantation” tests are poised for a dramatic leap forward as it becomes possible to peer more deeply at an embryo’s genome and create broad statistical forecasts about the person it would become.
The advance is occurring, say scientists, thanks to a growing flood of genetic data collected from large population studies. As statistical models known as predictors gobble up DNA and health information about hundreds of thousands of people, they’re getting more accurate at spotting the genetic patterns that foreshadow disease risk. But they have a controversial side, since the same techniques can be used to project the eventual height, weight, skin tone, and even intelligence of an IVF embryo.
In addition to Treff, who is the company’s chief scientific officer, the founders of Genomic Prediction are Stephen Hsu, a physicist who is vice president for research at Michigan State University, and Laurent Tellier, a Danish bioinformatician who is CEO. Both Hsu and Tellier have been closely involved with a project in China that aims to sequence the genomes of mathematical geniuses, hoping to shed light on the genetic basis of IQ.
Rare and extinct animals are preserved in jars of alcohol in natural history museum collections around the world, which provide a wealth of information on the changing biodiversity of the planet. These preserved specimens of snakes, lizards, frogs, fish and other animals can last up to 500 years when processed in a chemical called formalin. While formalin helps preserve the specimen making it rigid and durable, it poses a challenge to extracting and sequencing DNA. Furthermore, DNA degrades and splits into small fragments over time. This fragmented DNA is difficult to amplify into long informative stretches of DNA that can be used to examine evolutionary relationships among species when using older DNA sequencing technology. Therefore, scientists have not been able to effectively sequence DNA from these specimens until now.
LSU Museum of Natural Science Curator and Professor Christopher Austin and his collaborator Rutgers-Newark Assistant Professor Sara Ruane developed a protocol and tested a method for DNA sequencing thousands of genes from these intractable snake specimens. Their research was published today in the international scientific journal Molecular Ecology Resources.
“Natural history museums are repositories for extinct species. Unfortunately, naturalists in the 1800s were not collecting specimens for analyses we conduct today such as DNA sequencing. Now with these new methods, we can get the DNA from these very old specimens and sequence extinct species like the Ivory Billed Woodpecker, the Tasmanian Wolf and the Dodo Bird,” Austin said.
He and Ruane found and tested an approach that includes taking a small piece of liver tissue from the snake specimen, heating it up over a longer period of time and applying an enzyme that digests the tissue sample and enables the DNA to be extracted. Their minimally invasive protocol preserves the specimen so additional information can be collected from the specimen in the future. It also includes applying the latest technology to chemically sequence the specimens’ DNA.
“A genome is a complex jigsaw puzzle broken up in to hundreds of millions of small pieces. We can sequence those pieces and computationally put them back together,” Austin said.
They extracted and sequenced the DNA of 13 historic or rare snake specimens from all over the world many of which had never been analyzed using modern genetic methods. Some of the specimens were more than 100 years old. They also integrated these data with modern samples to create a genetic family tree, or phylogeny, that maps the evolutionary relationships of various snake species. This work resulted in thousands of genetic markers for snake specimens collected as far back as the early 1900s.
“The exciting thing about this work is that it makes species that have been essentially lost to science, due to extirpation, rarity or general secretiveness, which applies to many animals and not just snakes, available for scientific research in the modern age of genomics,” Ruane said.
“We also believe this research will benefit scientists working with rare animals that are either hard to collect or extinct but are represented in fluid-preserved historical collections. It also underscores the continued importance of museum collections in modern science,” Austin said.
A group coordinated by the International School for Advanced Studies (SISSA) in Trieste has built a three-dimensional computer model of the human genome. The shape of DNA (as well as its sequence) significantly affects biological processes and is therefore crucial for understanding its function. This new study has provided a first three-dimensional, approximate but realistic, identikit of the human genome. Thanks to the characteristics of the new method, the structural reconstruction based on both experimental information and statistical methods will be refined as new experimental data become available. The study, carried out in collaboration with the University of Oslo, has just been published in Scientific Reports (a journal of the Nature group).
Genome sequencing is a milestone in modern biology as it allows access to the entire “list of instructions” (the chemical sequence of genetic makeup) for the development and function of organisms. Sequencing the genome is a bit like writing down the exact order of the color of beads in a necklace: knowing how they are arranged along the thread gives us no indication as to the shape of the necklace. The shape of the DNA strand can be highly complex, given that the chromosomes are loosely arranged in an apparently chaotic tangle in the cell nucleus. Since the shape of chromosomes may have a decisive effect on their function, it is important that it should be characterized, in part because scientists think the DNA tangle in the nucleus is only apparently chaotic and that it has instead a specific “geography” for each tissue and stage of cell life.
“Arriving at a precise description of the shape of the DNA tangle is unfortunately incredibly complicated”, explains Cristian Micheletti, SISSA professor and coordinator of the new study. “In our case, we used experimental data on ‘proximity pairs’”. “Imagine having to create a map of a city”, he explains, “based only on information like ‘the post office is opposite the station’, ‘the chemist is close to the gym’, ‘the fruit and vegetable market is near the football field’ and so on. If you have only a small number of such statements to go by, your map will be approximate and in some cases indeterminate. But if you have hundreds, thousands or even more, then your map will become increasingly precise and accurate. This is the logic we followed”.
“Proximity pairs” therefore refers to information on the closeness of two points on the map. In the case of nuclear DNA, this information was provided by a technique (which Micheletti defines as “brilliant”) known as Hi-C, developed by North American research groups in 2010. In this chemical-physical technique, bits of genome located close to each another in the nucleus are tied together and then identified by their sequence. By collecting large numbers of these proximity pairs scientists discovered which points of the chromosomes lie close to each other in the nucleus. While this is today the most powerful technique for investigating DNA organisation in the nucleus, it is still inadequate for inferring its overall shape. “For this reason, we thought we would try to go ‘further’”, comments Micheletti.
Stephen Kingsmore, M.D., D.Sc., president and CEO of Rady Children's Institute for Genomic Medicine at Rady Children's Hospital – San Diego, is the official title holder of the Guinness World Records® designation for fastest genetic diagnosis, which he accomplished by successfully diagnosing critically ill newborns in just 26 hours, as published in the journal Genome Medicine.
The feat was made possible by several time-shrinking technologies, including Edico Genome's genomic data-crunching computer chip, DRAGEN, and one of Illumina's high-throughput sequencing instruments. In addition, other parameters of the sequencing process were optimized.
Dr. Kingsmore achieved this Guinness World Records title while serving as executive director of Medical Panomics at Children's Mercy Kansas City; he will implement the enabling technologies at the new Rady Children's Institute for Genomic Medicine. Today's celebration in San Diego, often called "the genomics capital of the world," is being held on National DNA Day, which commemorates the completion of the Human Genome Project and the discovery of DNA's double helix.
"Diagnosing acutely ill babies is a race against the clock, which is why it's so essential for physicians to have access to technology that will provide answers faster and help set the course of treatment," Dr. Kingsmore said. "My work at Children's Mercy Kansas City that led to this recognition would not have been possible without our key technology partners Edico Genome and Illumina, who share a vision for unraveling mysteries of disease and giving hope to families with ill newborns. I look forward to collaborating with both parties to implement this approach at Rady Children's Institute for Genomic Medicine and ultimately neonatal and pediatric intensive care units across the country."
MicroRNA (miRNA) are cellular fragments of RNA that in some organisms prevent the production of certain proteins. They have been found to be expressed in tissues of some non-mammals and in some embryos prior to pre-implantation. Little is known about their function in mammals, however, though prior research had found them to exist in oocytes (ovarian cells that lead to the development of an ovum) and early embryos. Prior efforts to deep sequence them in mammals has proved to be extremely challenging due to the numbers of them that must be processed, thus scientists still do not know what role they may play in embryo development, if any. In this new effort, the researchers report that they have found a way to tweak the cDNA library construction method for small RNAs resulting in a need for only 10 nanograms of RNA for doing a deep sequence, and because of that, were able to profile samples of both mouse oocytes and early embryos.
To tweak the construction method, the researchers optimized the 5' and 3' adaptor ligation and PCR amplification steps, which allowed for drastically reducing the amount of RNA needed. To test their ideas they performed the tweaking on 293 human embryonic kidney cells. Once they had the technique developed, they switched to testing mice oocytes and early embryos to learn more about the role of miRNA in mammal embryo development. They report that they were able to trace the processes surrounding miRNA as it moved from fertilization to early embryonic development—which was the first time that had ever been done. Furthermore, they found that the role miRNA played was suppressed as initial cell division was occurring—though it was not clear why that occurred—but later it was reactivated, perhaps as part of the process of regulating zygotic genetic growth factors.
Researchers at the National Institute of Standards and Technology (NIST) have simulated a new concept for rapid, accurate gene sequencing by pulling a DNA molecule through a tiny chemically activated hole in graphene—an ultrathin sheet of carbon atoms—and detecting changes in electrical current.
The NIST simulation study suggests the method could identify about 66 million bases per second with 90 percent accuracy and no false positives. If demonstrated experimentally, the NIST method might ultimately be faster and cheaper than conventional DNA sequencing, meeting a critical need for applications such as forensics.
Conventional sequencing, developed in the 1970s, involves separating, copying, labeling and reassembling pieces of DNA to read the genetic information. The new NIST proposal is a twist on the more recent “nanopore sequencing” idea of pulling DNA through a hole in specific materials, originally a protein (see “First full genome of a living organism sequenced and assembled using smartphone-size device“).
This concept—pioneered 20 years ago at NIST—is based on the passage of electrically charged particles (ions) through the pore. The idea remains popular but poses challenges such as unwanted electrical noise, or interference, and inadequate selectivity.
By contrast, NIST’s new proposal is to create temporary chemical bonds and rely on graphene’s capability to convert the mechanical strains (rather than charged particles) from breaking those bonds into measurable blips in electrical current.
“This is essentially a tiny strain sensor,” says NIST theorist Alex Smolyanitsky, who came up with the idea and led the project. “We did not invent a complete technology. We outlined a new physical principle that can potentially be far superior to anything else out there.”
Graphene is popular in nanopore-sequencing proposals due to its electrical properties and miniaturized thin-film structure. In the new NIST method, a graphene nanoribbon (4.5 by 15.5 nanometers) has several copies of a base attached to the nanopore (2.5 nm wide). DNA’s genetic code is built from four kinds of bases, which bond in pairs as cytosine–guanine and thymine–adenine.
In simulations of how the sensor would perform at room temperature in water, cytosine is attached to the nanopore to detect guanine. A single-strand (unzipped) DNA molecule is pulled through the pore. When guanine passes by, hydrogen bonds form with the cytosine. As the DNA continues moving, the graphene is yanked and then slips back into position as the bonds break.
The NIST study focused on how this strain affects graphene’s electronic properties and found that temporary changes in electrical current indeed indicate that a target base has just passed by. To detect all four bases, four graphene ribbons, each with a different base inserted in the pore, could be stacked vertically to create an integrated DNA sensor.
The researchers combined simulated data with theory to estimate levels of measurable signal variations. Signal strength was in the milliampere range, stronger than in the earlier ion-current nanopore methods.
Based on the performance of 90 percent accuracy without any false positives (i.e., errors were due to missed bases rather than wrong ones), the researchers suggest that four independent measurements of the same DNA strand would produce 99.99 percent accuracy, as required for sequencing the human genome.
The study authors concluded that the proposed method shows “significant promise for realistic DNA sensing devices” without the need for advanced data processing, microscopes, or highly restricted operating conditions. Other than attaching bases to the nanopore, all sensor components have been demonstrated experimentally by other research groups. Theoretical analysis suggests that basic electronic filtering methods could isolate the useful electrical signals. The proposed method could also be used with other strain-sensitive membranes, such as molybdenum disulfide.
“With our integrated graphene nanopore with plasmonic optical antenna, we can obtain direct optical DNA sequence detection,” says Luke Lee, the Arnold and Barbara Silverman Distinguished Professor at UC Berkeley. Lee and Alex Zettl, a physicist who holds joint appointments with Berkeley Lab’s Materials Sciences Division and UC Berkeley’s Physics Department, were the leaders of a study in which a hot spot on a graphene membrane formed a nanopore with a self-integrated optical antenna. The hot spot was created by photon-to-heat conversion of a gold nanorod.
“We believe our approach opens new avenues for simultaneous electrical and optical nanopore DNA sequencing and for regulating DNA translocation,” says Zettl, who is also a member of the Kavli Energy Nanoscience Institute (Kavli ENSI).
Nanopore sequencing of DNA, in which DNA strands are threaded through nanoscale pores and read one letter at a time, has been touted for its ability to make DNA sequencing a faster and more routine procedure. Under today’s technology, the DNA letters are “read” by an electrical current passing through nanopores fabricated on a silicon chip. Trying to read electrical signals from DNA passing through thousands of nanopores at once, however, can result in major bottlenecks. Adding an optical component to this readout would help eliminate such bottlenecks
“Direct and enhanced optical signals are obtained at the junction of a nanopore and its optical antenna,” says Lee. “Simultaneously correlating this optical signal with the electrical signal from conventional nanopore sequencing provides an added dimension that would be an enormous advantage for high-throughput DNA readout.”
A key to the success of this effort is the single-step photothermal mechanism that enables the creation of graphene nanopores with self-aligned plasmonic optical antennas. The dimensions of the nanopores and the optical characteristics of the plasmonic antenna are tunable, with the antenna functioning as both optical signal transducer and enhancer. The atomically thin nature of the graphene membrane makes it ideal for high resolution, high throughput, single-molecule DNA sequencing. DNA molecules can be labeled with fluorescent dyes so that each base-pair fluoresces at a signature intensity as it passes through the junction of the nanopore and its optical antenna.
“In addition, either the gold nanoplasmonic optical antenna or the graphene can be functionalized to be responsive to different base-pair combinations,” Lee says. “The gold plasmonic optical antenna can also be functionalized to enable the direct optical detection of RNA, proteins, protein-protein interactions, DNA-protein interactions, and other biological systems.”
A University of Central Florida research team has developed a facial recognition tool that promises to be useful in rapidly matching pictures of children with their biological parents and in potentially identifying photos of missing children as they age.
The work verifies that a computer is capable of matching pictures of parents and their children. The study will be presented at the nation's premier event for the science of computer vision - the IEEE Computer Vision and Pattern Recognition conference in Columbus, Ohio, which begins Monday, June 23. Graduate Student Afshin Dehfghan and a team from UCF's Center for Research in Computer Vision started the project with more than 10,000 online images of celebrities, politicians and their children.
"We wanted to see whether a machine could answer questions, such as 'Do children resemble their parents?' 'Do children resemble one parent more than another?' and 'What parts of the face are more genetically inspired?'" he said.
Anthropologists have typically studied these questions. However Dehghan and his team are advancing a new wave of computational science that uses the power of a mechanical "mind" to evaluate data completely objectively – without the clutter of subjective human emotions and biases. The tool could be useful to law enforcement and families in locating missing children.
"As this tool is developed I could see it being used to identify long-time missing children as they mature," said Ross Wolf, associate professor of criminal justice at UCF.
Wolf said that facial recognition technology is already heavily used by law enforcement, but that it has not been developed to the point where it can identify the same characteristics in photos over time, something this technology could have the capability to do. Dehghan said he is planning to expand on the work in that area by studying how factors such as age and ethnicity affect the resemblance of facial features.
Computer program crudely predicts a facial structure from genetic variations.
Researchers have now shown how 24 gene variants can be used to construct crude models of facial structure. Thus, leaving a hair at a crime scene could one day be as damning as leaving a photograph of your face. Researchers have developed a computer program that can create a crude three-dimensional (3D) model of a face from a DNA sample.
Using genes to predict eye and hair color is relatively easy. But the complex structure of the face makes it more valuable as a forensic tool — and more difficult to connect to genetic variation, says anthropologist Mark Shriver of Pennsylvania State University in University Park, who led the work, published today in PLOS Genetics1.
Shriver and his colleagues took high-resolution images of the faces of 592 people of mixed European and West African ancestry living in the United States, Brazil and Cape Verde. They used these images to create 3D models, laying a grid of more than 7,000 data points on the surface of the digital face and determining by how much particular points on a given face varied from the average: whether the nose was flatter, for instance, or the cheekbones wider. They had volunteers rate the faces on a scale of masculinity and femininity, as well as on perceived ethnicity.
Next, the authors compared the volunteers’ genomes to identify points at which the DNA differed by a single base, called a single nucleotide polymorphism (SNP). To narrow down the search, they focused on genes thought to be involved in facial development, such as those that shape the head in early embryonic development, and those that are mutated in disorders associated with features such as cleft palate. Then, taking into account the person’s sex and ancestry, they calculated the statistical likelihood that a given SNP was involved in determining a particular facial feature.
This pinpointed 24 SNPs across 20 genes that were significantly associated with facial shape. A computer program the team developed using the data can turn a DNA sequence from an unknown individual into a predictive 3D facial model (see 'Face to face'). Shriver says that the group is now trying to integrate more people and genes, and look at additional traits, such as hair texture and sex-specific differences.
Researchers led by bioengineers at the University of California, San Diego have generated the most complete genome sequences from single E. coli cells and individual neurons from the human brain. The breakthrough comes from a new single-cell genome sequencing technique that confines genome amplification to fluid-filled wells with a volume of just 12 nanoliters. "Our preliminary data suggest that individual neurons from the same brain have different genetic compositions. This is a relatively new idea, and our approach will enable researchers to look at genomic differences between single cells with much finer detail," said Kun Zhang, a professor in the Department of Bioengineering at the UC San Diego Jacobs School of Engineering and the corresponding author on the paper.
The researchers report that the genome sequences of single cells generated using the new approach exhibited comparatively little "amplification bias," which has been the most significant technological obstacle facing single-cell genome sequencing in the past decade. This bias refers to the fact that the amplification step is uneven, with different regions of a genome being copied different numbers of times. This imbalance complicates many downstream genomic analyses, including assembly of genomes from scratch and identifying DNA content variations among cells from the same individual.
Sequencing the genomes of single cells is of great interest to researchers working in many different fields. For example, probing the genetic make-up of individual cells would help researchers identify and understand a wide range of organisms that cannot be easily grown in the lab from the bacteria that live within our digestive tracts and on our skin, to the microscopic organisms that live in ocean water. Single-cell genetic studies are also being used to study cancer cells, stem cells and the human brain, which is made up of cells that increasingly appear to have significant genomic diversity.
DNA sequencing is the process of determining the precise order of nucleotides within a DNA molecule. It includes any method or technology that is used to determine the order of the four bases in a strand of DNA. The advent of rapid DNA sequencing methods has greatly accelerated biological and medical research and discovery.
Knowledge of DNA sequences has become indispensable for basic biological research, and in numerous applied fields such as diagnostic, and biological systemathics. The rapid speed of sequencing attained with modern DNA sequencing technology has been instrumental in the sequencing of complete DNA sequences, or genomes of numerous types and species of life, including the human genome and other complete DNA sequences of many animal, plant, and microbiall species.
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