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Commercially viable biofuel crops are vital to reducing greenhouse gas emissions, and a new tool developed by the Center for Advanced Bioenergy and Bioproducts Innovation should accelerate their development -; as well as genetic editing advances overall. CROPSR, the first open-source software tool for genome-wide design and evaluation of guide RNA (gRNA) sequences for CRISPR experiments, created by scientists at CABBI, a Department of Energy-funded Bioenergy Research Center (BRC). The genome-wide approach significantly shortens the time required to design a CRISPR experiment, reducing the challenge of working with crops and accelerating gRNA sequence design, evaluation, and validation, according to the study published in BMC Bioinformatics. "CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments," said CROPSR developer Hans Müller Paul, a molecular biologist and Ph.D. student with co-author Matthew Hudson, Professor of Crop Sciences at the University of Illinois Urbana-Champaign. "We hope that the new software will accelerate discovery and reduce the number of failed experiments." CROPSR developer Hans Müller Paul, a molecular biologist and Ph.D. student with co-author Matthew Hudson, Professor of Crop Sciences at the University of Illinois Urbana-Champaign. To better meet the needs of crop geneticists, the team built software that lifts restrictions imposed by other packages on design and evaluation of gRNA sequences, the guides used to locate targeted genetic material. Team members also developed a new machine learning model that would not avoid guides for repetitive genomic regions often found in plants, a problem with existing tools. The CROPSR scoring model provided much more accurate predictions, even in non-crop genomes, the authors said.
Via BigField GEG Tech
The latest in high-density ultra-durable data storage has been perfected over billions of years by nature itself. Now ‘Smoke on the Water’ is making history again. This September, it was one of the first items from the Memory Of the World archive to be stored in the form of DNA and then played back with 100% accuracy. The project was a joint effort between the University of Washington, Microsoft and Twist Bioscience, a San Francisco-based DNA manufacturing company. The demonstration was billed as a ‘proof of principle’ – which is shorthand for successful but too expensive to be practical. At least for now. Many pundits predict it’s just a matter of time till DNA pips magnetic tape as the ultimate way to store data. It’s compact, efficient and resilient. After all, it has been tweaked over billions of years into the perfect repository for genetic information. It will never become obsolete, because as long as there is life on Earth, we will be interested in decoding DNA. “Nature has optimised the format,” says Twist Bioscience’s chief technology officer Bill Peck. Players like Microsoft, IBM and Intel are showing signs of interest. In April, they joined other industry, academic and government experts at an invitation-only workshop (cosponsored by the U.S. Intelligence Advanced Research Projects Activity (IARPA)) to discuss the practical potential for DNA to solve humanity’s looming data storage crisis. It’s a big problem that’s getting bigger by the minute. According to a 2016 IBM Marketing Cloud report, 90% of the data that exists today was created in just the past two years. Every day, we generate another 2.5 quintillion (2.5 × 1018) bytes of information. It pours in from high definition video and photos, Big Data from particle physics, genomic sequencing, space probes, satellites, and remote sensing; from think tanks, covert surveillance operations, and Internet tracking algorithms. EVERY DAY, WE GENERATE ANOTHER 2.5 QUINTILLION BYTES OF INFORMATION. Right now all those bits and bytes flow into gigantic server farms, onto spinning hard drives or reels of state-of-the-art magnetic tape. These physical substrates occupy a lot of space. Compare this to DNA. The entire human genome, a code of three billion DNA base pairs, or in data speak, 3,000 megabytes, fits into a package that is invisible to the naked eye – the cell’s nucleus. A gram of DNA — the size of a drop of water on your fingertip — can store at least the equivalent of 233 computer hard drives weighing more than 150 kilograms. To store the all the genetic information in a human body — 150 zettabytes — on tape or hard drives, you’d need a facility covering thousands, if not millions of square feet. And then there’s durability. Of the current storage contenders, magnetic tape has the best lifespan, at about 10-20 years. Hard drives, CDs, DVDs and flash drives are less reliable, often failing within five to ten years. DNA has proven that it can survive thousands of years unscathed. In 2013, for example, the genome of an early horse relative was reconstructed from DNA from a 700,000-year-old bone fragment found in the Alaskan permafrost.
Via Integrated DNA Technologies
Pharmaceutical companies typically develop new drugs with thousands of staff and budgets that run into the billions of dollars. One estimate puts the cost of bringing a new drug to market at $2.6 billion with others suggesting that it could be double that cost at $5 billion. One man, Professor Atul Butte director of the University of California Institute of Computational Health Sciences, believes that like other Silicon Valley startups, almost anyone can bring a drug to market from their garage with just a computer, the internet, and freely available data. In a talk given at the Science on the Swan conference held in Perth this week, Professor Butte outlined the process for an audience of local and international scientists and medics. The starting point is the genetic data from thousands of studies on humans, mice and other animals, that is now freely available on sites from the National Institute of Health and the European Molecular Biology Laboratory. The proliferation of genetic data from experiments has been driven by the ever decreasing cost of sequencing genetic information using gene chip technologies. Professor Butte, students, and research staff have found a range of different ways of using this data to look for new drugs. In one approach, they have constructed a map of how the genetic profiles of people with particular diseases are related to each other. In particular, to look for diseases with very similar genetic profiles. Having done that, they noticed that the genetic profile of people with heart conditions were very closely related to that of the much rarer condition of muscular dystrophy. What this potentially suggested was that drugs that work for one condition could potentially work in the other. This process of discovering other uses of drugs, called “drug repositioning”, is not new. Drugs like Viagra were originally used for treatment of cardiovascular conditions. The difference is that Viagra’s repositioned use resulted from the observation of side-effects in patients taking the drug for its original intended purpose. Professor Butte on the other hand is using “Big Data” and computers to show that given the close relationship in the genetic profile of two diseases, the potential cross-over effect of drugs working for one condition working in another. Still in the garage, the next step from discovering a potential drug is to test if it actually works in an experimental setting on animals. Here again, Professor Butte has turned to the internet and sites like Assay Depot. This is a site, structured like Amazon, from which a researcher can order an experiment to be carried out to test a drug on a range of animal models. It is literally a case of choosing the experiment type you want, adding it to a shopping cart, paying by credit card and getting the experimental results mailed back in a few weeks time. “Shoppers” are given the choice of laboratory they want to use, including a choice of which country the lab is based. Once a new use for a drug has been shown to work in an animal model, the next step would be to test the drug in humans, get approval for the use of the drug for that condition and then finally take the drug to market.
DNA’s unique structure is ideal for carrying genetic information, but scientists have recently found ways to exploit this versatile molecule for other purposes: By controlling DNA sequences, they can manipulate the molecule to form many different nanoscale shapes. Chemical and molecular engineers at MIT and Harvard University have now expanded this approach by using folded DNA to control the nanostructure of inorganic materials. After building DNA nanostructures of various shapes, they used the molecules as templates to create nanoscale patterns on sheets of graphene. This could be an important step toward large-scale production of electronic chips made of graphene, a one-atom-thick sheet of carbon with unique electronic properties. “This gives us a chemical tool to program shapes and patterns at the nanometer scale, forming electronic circuits, for example,” says Michael Strano, a professor of chemical engineering at MIT and a senior author of a paper describing the technique in the April 9 issue of Nature Communications. Peng Yin, an assistant professor of systems biology at Harvard Medical School and a member of Harvard’s Wyss Institute for Biologically Inspired Engineering, is also a senior author of the paper, and MIT postdoc Zhong Jin is the lead author. Other authors are Harvard postdocs Wei Sun and Yonggang Ke, MIT graduate students Chih-Jen Shih and Geraldine Paulus, and MIT postdocs Qing Hua Wang and Bin Mu. Most of these DNA nanostructures are made using a novel approach developed in Yin’s lab. Complex DNA nanostructures with precisely prescribed shapes are constructed using short synthetic DNA strands called single-stranded tiles. Each of these tiles acts like an interlocking toy brick and binds with four designated neighbors. Using these single-stranded tiles, Yin’s lab has created more than 100 distinct nanoscale shapes, including the full alphabet of capital English letters and many emoticons. These structures are designed using computer software and can be assembled in a simple reaction. Alternatively, such structures can be constructed using an approach called DNA origami, in which many short strands of DNA fold a long strand into a desired shape. However, DNA tends to degrade when exposed to sunlight or oxygen, and can react with other molecules, so it is not ideal as a long-term building material. “We’d like to exploit the properties of more stable nanomaterials for structural applications or electronics,” Strano says. Instead, he and his colleagues transferred the precise structural information encoded in DNA to sturdier graphene. The chemical process involved is fairly straightforward, Strano says: First, the DNA is anchored onto a graphene surface using a molecule called aminopyrine, which is similar in structure to graphene. The DNA is then coated with small clusters of silver along the surface, which allows a subsequent layer of gold to be deposited on top of the silver. Once the molecule is coated in gold, the stable metallized DNA can be used as a mask for a process called plasma lithography. Oxygen plasma, a very reactive “gas flow” of ionized molecules, is used to wear away any unprotected graphene, leaving behind a graphene structure identical to the original DNA shape. The metallized DNA is then washed away with sodium cyanide.
A journal published a paper today that reveals a set of folding constraints in the design of RNA molecules. So far, so normal. Most of the data for the study come from an online game that crowdsources solutions from thousands of nonexpert players—unusual but not unique. But the lead authors of the paper are the players themselves. Now that is a first. And there's a twist: The journal nearly delayed publication because of "ethical" concerns about authors using only their game names.
The game is called Eterna, and it made a big splash in 2014 with a paper in the Proceedings of the National Academy of Sciences that had 37,000 players as co-authors. The goal was to see whether nonexpert humans can do better than computer algorithms at designing RNA sequences that fold into particular shapes. And indeed, the humans won, even after the computer algorithms were endowed with insights from the human folders. When it comes to the biophysics of RNA folding, John Henry still beats the machine.
That 2014 study was led by card-carrying scientists Adrien Treuille and Rhiju Das, biophysicists at Carnegie Mellon University in Pittsburgh, Pennsylvania, and Stanford University in Palo Alto, California, respectively. The two researchers created the game in 2009. (They both cut their teeth on scientific game design as postdocs in the lab of David Baker at the University of Washington, Seattle, where the blockbuster game FoldIt was conceived.) Since then they have massively scaled up the process and hooked the game to a real-world automated lab that actually tests the folding predictions made by players against the 3D structure of the RNA molecules. They call it the Eterna Massive Open Laboratory.
The newest paper shows how far the effort has come. Among the game's thousands of RNA design "puzzles," there seem to be a small set that are particularly difficult. Among the most challenging structural features to figure out is symmetry, where an RNA strand folds into two or more identically shaped loops. The Eterna game includes an interface for players to propose hypotheses about how particular RNA structures will or will not fold into particular shapes. Those were distilled into a set of "designability" rules. The question was: Do only human designers struggle with thorny design problems, or do computer simulations tussle too?
The answer is that the computers struggled just as much as the people. Researchers report that three of the best existing computer algorithms, running on a supercomputer at Stanford, struggled to solve the very same RNA design problems as the humans. The result shows that the human "designability" rules do indeed correspond to problems that are hard not just for human brains but also for computers, the team reports today in the Journal of Molecular Biology. In fact, the hardest puzzles that could be solved by experienced Eterna players were unsolvable by the computer even after days of crunching. And to help improve the algorithms, computer scientists now have a set of benchmarks—the Eterna100—to gauge the design difficulty of RNA structures.
One of the great challenges in molecular biology is to determine the three-dimensional structure of large biomolecules such as proteins. But this is a famously difficult and time-consuming task. The standard technique is x-ray crystallography, which involves analyzing the x-ray diffraction pattern from a crystal of the molecule under investigation. That works well for molecules that form crystals easily.
But many proteins, perhaps most, do not form crystals easily. And even when they do, they often take on unnatural configurations that do not resemble their natural shape. So finding another reliable way of determining the 3-D structure of large biomolecules would be a huge breakthrough. Today, Marcus Brubaker and a couple of pals at the University of Toronto in Canada say they have found a way to dramatically improve a 3-D imaging technique that has never quite matched the utility of x-ray crystallography.
The new technique is based on an imaging process called electron cryomicroscopy. This begins with a purified solution of the target molecule that is frozen into a thin film just a single molecule thick. This film is then photographed using a process known as transmission electron microscopy—it is bombarded with electrons and those that pass through are recorded. Essentially, this produces two-dimensional “shadowgrams” of the molecules in the film. Researchers then pick out each shadowgram and use them to work out the three-dimensional structure of the target molecule.
This process is hard for a number of reasons. First, there is a huge amount of noise in each image so even the two-dimensional shadow is hard to make out. Second, there is no way of knowing the orientation of the molecule when the shadow was taken so determining the 3-D shape is a huge undertaking.
The standard approach to solving this problem is little more than guesswork. Dream up a potential 3-D structure for the molecule and then rotate it to see if it can generate all of the shadowgrams in the dataset. If not, change the structure, test it, and so on.
Obviously, this is a time-consuming process. The current state-of-the-art algorithm running on 300 cores takes two weeks to find the 3-D structure of a single molecule from a dataset of 200,000 images.
Roughly 40 percent of all medications act on cells' G protein-coupled receptors. One of these receptors, beta 2 adrenergic receptor site (B2AR), naturally transforms between two base configurations; knowing the precise location of each of approximately 4,000 atoms is crucial for ensuring a snug fit between it and the drug.
Now, researchers at Stanford and Google have conducted an unprecedented, atom-scale simulation of the receptor site's transformation, a feat that could have significant impact on drug design. This is the first scientific project to be completed using Google Exacycle's cloud computing platform, which allows scientists to crunch big data on Google's servers during periods of low network demand.
The study was published in the January issue of Nature Chemistry. As a type of GPCR, the B2AR is a molecule that sits within the membrane of most cells. Various molecules in the body interact with the receptor's exterior, like two hands shaking, to trigger an action inside the cell.
"GPCRs are the gateway between the outside of the cell and the inside," said co-author Vijay Pande, PhD, professor of chemistry and a senior author of the study. "They're so important for biology, and they're a natural, existing signaling pathway for drugs to tap into."
Lead authors of the study were former postdoctoral scholar Kai Kohlhoff, PhD, and current postdoctoral scholars Diwakar Shukla, PhD, and Morgan Lawrenz, PhD.
Roughly half of all known drugs—including pharmaceuticals and naturally occuring molecules, such as caffeine —target some GPCR, and many new medications are being designed with these receptor sites in mind. Brian Kobilka, professor of molecular and cellular physiology at Stanford, was awarded the 2012 Nobel Prize in Chemistry for his role in discovering and understanding GPCRs.
Traditionally, maps that detail each atom of GPCRs and other receptors are created through a technique called X-ray crystallography. The technique is industry standard, but it can only visualize a molecule in its resting state; receptors naturally change configurations, and their intermediate forms might also have medical potential.
When developing a drug, scientists will often run a computer program, known as a docking program, that predicts how well the atomic structure of a proposed drug will fit into the known receptor.
In the case of GPCRs, for example, the X-ray crystallography techniques have detailed their "on" and "off" configurations; many medications have been specifically designed to fit into these sites. Scientists expect, however, that other fruitful configurations exist. Many drugs engage with GPCR sites, even though computational models suggest that they don't fit either of the two defined reaction site configurations.
The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein–small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medica and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Tinberg and Sagar D. Khare headed the study under the direction of David Baker, UW professor of biochemistry and Howard Hughes Medical Institute investigator, describe in a recent Nature paper a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG). Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and β-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics. The scientific team overcame previously unsolved problems in building accurate protein-small molecule interfaces. Earlier attempts struggled with discrepancies between the computer plans and the structures of the actual molecules. In conducting the study, the researchers learned general principles for engineering small molecule-binding proteins with strong attraction energies. Their findings open up the possibility that binding proteins could be created for many medical, industrial and environmental uses. In medical diagnostics, for example, a rationally programmed protein might detect biomolecules found only in a specific disease state, such as an early-stage cancer. Other types of protein molecules might eventually be manufactured to treat an overdose or to block a poison. Remediation possibilities for these molecular workhorses could include trapping pollutants or capturing waste. Tinberg explained that generation of novel small-molecule binding proteins currently consists of immunizing an animal to generate antibodies against a target protein, or directing the evolution of proteins in a laboratory to strengthen their affinity for the desired small-molecule. “Neither of these methods allows complete control over the interactions involved in binding,” she said. In designing their molecules, the team sought to replicate properties of a naturally occurring protein binding site. These are: specific interactions that enforce a strong attraction with the desired small molecule, a receptive shape to accept the small molecule, and an orderly structure, prepared for occupancy. The exclusive, move-in ready set up reduces the energy penalty by preventing the protein from having to change shape to accept the small molecule. This is in contrast to a flexible site, which is more disordered in the absence of the small molecule and has to freeze into one state upon binding.
American Internet Services (AIS - http://tinyurl.com/d6nu895), a leading provider of enterprise-class data center, cloud and connectivity services, today announced AIS BusinessCloud1 (BC1) Infrastructure as a Service (IaaS) based on the widely used VMware software suite with state-of-the-art compute and storage technology featuring Cisco, Dell, Arista and NetApp. AIS removes cost barriers so companies can quickly and efficiently migrate to the cloud by gaining access to compute and storage on demand. AIS BusinessCloud1 is San Diego's first full-featured VMware-based cloud service. AIS selected VMware for a number of reasons, among them are its widespread adoption, inherent functionality, and its robust ecosystem of support and product development. Diagnomics (http://www.diagnomics.com), a provider of complete personal genome sequencing and bioinformatics solutions to biomedical researchers, has been working closely with AIS to develop a cloud-based solution for large scale, data intensive genome annotation and storage to help resolve a major bottleneck in life science research. "The cloud I/O of AIS BusinessCloud1has significantly exceeded our expectations compared to a standard server cluster that is both more expensive to deploy and maintain," said Min Lee, chief executive officer at Diagnomics. "By partnering with AIS, Diagnomics has achieved flexibility that is required so that genome annotation can take the next step towards delivering on the vision of personalized medicine." AIS BC1 was designed with security, performance, and redundancy in mind: • No single points of failure (SPoF).Integrated VMware network and storage Quality of Service (QoS) feature set to production service standards. • Network storage is redundant, diversified, and optimized for performance. • Self-healing systems architecture with automated failover. •High-speed network access provided via the AIS regional optical and transit network. Some of the ways AIS BusinessCloud1 can be used include: • As an IT Extension where a direct connection can be added to the company's current VMware infrastructure and then be managed using vCenter.
As a Hybrid Cloud Solution providing instant IT scalability with built-in firewalling, a single management console, and complete public / private network isolation. As a Zero CAPEX disaster recovery site that can mirror data between sites with full infrastructure duplication. Companies can leverage the AIS 10GigE transport to the AIS Van Buren Data Center (VBDC) in Phoenix -- considered to be one of the safest locations in the nation in terms of protection from natural disasters.
Computer game crowdsources DNA sequence alignments across different species. The hope that swarms of gamers can help to solve difficult biological problems has been given another boost by a report in the journal PLoS One1, showing that data gleaned from the online game Phylo are helping to untangle a major problem in comparative genomics. The game was created to address the 'multiple sequence alignment (MSA) problem', which refers to the difficulty of aligning roughly similiar sequences of DNA in genes common to many species. A DNA sequence that is conserved across species suggests that it plays an important role in the ultimate function of that particular gene. http://tinyurl.com/72lxreb
Cancer Research UK scientists have mapped the first 3D structure of c-Cbl, a key protein that protects against the development of cancer. c-Cbl controls cell growth , which when unregulated causes cells to divide excessively and can lead to cancer. The protein is defective in some leukemia patients. Discovering that c-Cbl can switch between two shapes may help scientists find ways to prevent faulty c-Cbl from triggering cancer.
Roughly 17,000 registered players of the DNA sequencing game Phylo have helped solve more than 350,000 problems since November 2010. Read this blog post by Elizabeth Armstrong Moore on Health Tech.
Via Dr Richard Badge
Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of plugins are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.
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Synthetic biologists are converting microbial cells into living devices that are able to perform useful tasks ranging from the production of drugs, fine chemicals and biofuels to detecting disease-causing agents and releasing therapeutic molecules inside the body. To accomplish this, they fit cells with artificial molecular machinery that can sense stimuli such as toxins in the environment, metabolite levels or inflammatory signals. Much like electronic circuits, these synthetic biological circuits can process information and make logic-guided decisions. Unlike their electronic counterparts, however, biological circuits must be fabricated from the molecular components that cells can produce, and they must operate in the crowded and ever-changing environment within each cell. Similar to how computer scientists use logical language to have their programs make accurate AND, OR and NOT decisions towards a final goal, “Ribocomputing Devices” (stylized here in yellow) developed by a team at the Wyss Institute can now be used by synthetic biologists to sense and interpret multiple signals in cells and logically instruct their ribosomes (stylized in blue and green) to produce different proteins. So far, synthetic biological circuits can only sense a handful of signals, giving them an incomplete picture of conditions in the host cell. They are also built out of several moving parts in the form of different types of molecules, such as DNAs, RNAs, and proteins, that must find, bind and work together to sense and process signals. Identifying molecules that cooperate well with one another is difficult and makes development of new biological circuits a time-consuming and often unpredictable process. As reported in Nature, a team at Harvard’s Wyss Institute for Biologically Inspired Engineering is now presenting an all-in-one solution that imbues a molecule of ‘ribo’ nucleic acid or RNA with the capacity to sense multiple signals and make logical decisions to control protein production with high precision. The study’s approach resulted in a genetically encodable RNA nano-device that can perform an unprecedented 12-input logic operation to accurately regulate the expression of a fluorescent reporter protein in E. coli bacteria only when encountering a complex, user-prescribed profile of intra-cellular stimuli. Such programmable nano-devices may allow researchers to construct more sophisticated synthetic biological circuits, enabling them to analyze complex cellular environments efficiently and to respond accurately. “We demonstrate that an RNA molecule can be engineered into a programmable and logically acting “Ribocomputing Device,” said Wyss Institute Core Faculty member Peng Yin, Ph.D., who led the study and is also Professor of Systems Biology at Harvard Medical School. “This breakthrough at the interface of nanotechnology and synthetic biology will enable us to design more reliable synthetic biological circuits that are much more conscious of the influences in their environment relevant to specific goals.”
Modern archiving technology cannot keep up with the growing tsunami of bits. But nature may hold an answer to that problem already. For Nick Goldman, the idea of encoding data in DNA started out as a joke. It was Wednesday 16 February 2011, and Goldman was at a hotel in Hamburg, Germany, talking with some of his fellow bioinformaticians about how they could afford to store the reams of genome sequences and other data the world was throwing at them. He remembers the scientists getting so frustrated by the expense and limitations of conventional computing technology that they started kidding about sci-fi alternatives. “We thought, 'What's to stop us using DNA to store information?'” Then the laughter stopped. “It was a lightbulb moment,” says Goldman, a group leader at the European Bioinformatics Institute (EBI) in Hinxton, UK. True, DNA storage would be pathetically slow compared with the microsecond timescales for reading or writing bits in a silicon memory chip. It would take hours to encode data by synthesizing DNA strings with a specific pattern of bases, and still more hours to recover that information using a sequencing machine. But with DNA, a whole human genome fits into a cell that is invisible to the naked eye. For sheer density of information storage, DNA could be orders of magnitude beyond silicon — perfect for long-term archiving. “We sat down in the bar with napkins and biros,” says Goldman, and started scribbling ideas: “What would you have to do to make that work?” The researchers' biggest worry was that DNA synthesis and sequencing made mistakes as often as 1 in every 100 nucleotides. This would render large-scale data storage hopelessly unreliable — unless they could find a workable error-correction scheme. Could they encode bits into base pairs in a way that would allow them to detect and undo the mistakes? “Within the course of an evening,” says Goldman, “we knew that you could.” He and his EBI colleague Ewan Birney took the idea back to their labs, and two years later announced that they had successfully used DNA to encode five files, including Shakespeare's sonnets and a snippet of Martin Luther King's 'I have a dream' speech1. By then, biologist George Church and his team at Harvard University in Cambridge, Massachusetts, had unveiled an independent demonstration of DNA encoding2. But at 739 kilobytes (kB), the EBI files comprised the largest DNA archive ever produced — until July 2016, when researchers from Microsoft and the University of Washington claimed a leap to 200 megabytes (MB). The latest experiment signals that interest in using DNA as a storage medium is surging far beyond genomics: the whole world is facing a data crunch. Counting everything from astronomical images and journal articles to YouTube videos, the global digital archive will hit an estimated 44 trillion gigabytes (GB) by 2020, a tenfold increase over 2013. By 2040, if everything were stored for instant access in, say, the flash memory chips used in memory sticks, the archive would consume 10–100 times the expected supply of microchip-grade silicon3.
If DNA archives become a plausible method of data storage, it will be thanks to rapid advances in genetic technologies. The sequencing machines that “read out” DNA code have already become exponentially faster and cheaper; the National Institutes of Health shows costs for sequencing a 3-billion-letter genome plummeting from US $100 million in 2001 to a mere $1,000 today. However, DNA synthesis technologies required to “write” the code are much newer and less mature. Synthetic-biology companies like San Francisco’s Twist Biosciencehave begun manufacturing DNA to customers’ specifications only in the last few years, primarily serving biotechnology companies that are tweaking the genomes of microbes to trick them into making some desirable product. Manufacturing DNA for data storage could be a profitable new market, says Twist CEO Emily Leproust. Twist sent a representative to the April meeting, and the company is also working with Microsoft on a separate experiment in DNA storage, in which it synthesized 10 million strands of DNA to encode Microsoft’s test file. Leproust says Microsoft and the other tech companies are currently trying to determine “what kind of R&D has to be done to make a viable commercial product.” To make a product that’s competitive with magnetic tape for long-term storage, Leproust estimates that the cost of DNA synthesis must fall to 1/10,000 of today’s price. “That is hard,” she says mildly. But, she adds, her industry can take inspiration from semiconductor manufacturing, where costs have dropped far more dramatically. And just last month, an influential group of geneticists proposed an international effort to reduce the cost of DNA synthesis, suggesting that $100 million could launch the project nicely.
A team of scientists from Berkeley Lab, JGI and UC Berkeley, simplified and sped up genome assembly, reducing a months-long process to mere minutes. This was primarily achieved by “parallelizing” the code to harness the processing power of supercomputers, such as NERSC’s Edison system. Genomes are like the biological owner’s manual for all living things. Cells read DNA instantaneously, getting instructions necessary for an organism to grow, function and reproduce. But for humans, deciphering this “book of life” is significantly more difficult. Nowadays, researchers typically rely on next-generation sequencers to translate the unique sequences of DNA bases (there are only four) into letters: A, G, C and T. While DNA strands can be billions of bases long, these machines produce very short reads, about 50 to 300 characters at a time. To extract meaning from these letters, scientists need to reconstruct portions of the genome—a process akin to rebuilding the sentences and paragraphs of a book from snippets of text. But this process can quickly become complicated and time-consuming, especially because some genomes are enormous. For example, while the human genome contains about 3 billion bases, the wheat genome contains nearly 17 billion bases and the pine genome contains about 23 billion bases. Sometimes the sequencers will also introduce errors into the dataset, which need to be filtered out. And most of the time, the genomes need to be assembled de novo, or from scratch. Think of it like putting together a ten billion-piece jigsaw puzzle without a complete picture to reference. By applying some novel algorithms, computational techniques and the innovative programming language Unified Parallel C (UPC) to the cutting-edge de novo genome assembly tool Meraculous, a team of scientists from the Lawrence Berkeley National Laboratory (Berkeley Lab)’s Computational Research Division (CRD), Joint Genome Institute (JGI) and UC Berkeley, simplified and sped up genome assembly, reducing a months-long process to mere minutes. This was primarily achieved by “parallelizing” the code to harness the processing power of supercomputers, such as the National Energy Research Scientific Computing Center’s (NERSC’s) Edison system. Put simply, parallelizing code means splitting up tasks once executed one-by-one and modifying or rewriting the code to run on the many nodes (processor clusters) of a supercomputer all at once. “Using the parallelized version of Meraculous, we can now assemble the entire human genome in about eight minutes using 15,360 computer processor cores. With this tool, we estimate that the output from the world’s biomedical sequencing capacity could be assembled using just a portion of NERSC’s Edison supercomputer,” says Evangelos Georganas, a UC Berkeley graduate student who led the effort to parallelize Meraculous. He is also the lead author of a paper published and presented at the SC Conference in November 2014. “This work has dramatically improved the speed of genome assembly,” says Leonid Oliker computer scientist in CRD. “The new parallel algorithms enable assembly calculations to be performed rapidly, with near linear scaling over thousands of cores. Now genomics researchers can assemble large genomes like wheat and pine in minutes instead of months using several hundred nodes on NERSC’s Edison.”
Sensors, memory switches, and circuits can be encoded in a common gut bacterium.
The “friendly” bacteria inside our digestive systems are being given an upgrade, which may one day allow them to be programmed to detect and ultimately treat diseases such as colon cancer and immune disorders.
In a paper published today in the journal Cell Systems, researchers at MIT unveil a series of sensors, memory switches, and circuits that can be encoded in the common human gut bacterium Bacteroides thetaiotaomicron.
These basic computing elements will allow the bacteria to sense, memorize, and respond to signals in the gut, with future applications that might include the early detection and treatment of inflammatory bowel disease or colon cancer.
Researchers have previously built genetic circuits inside model organisms such as E. coli. However, such strains are only found at low levels within the human gut, according to Timothy Lu, an associate professor of biological engineering and of electrical engineering and computer science, who led the research alongside Christopher Voigt, a professor of biological engineering at MIT.
“We wanted to work with strains like B. thetaiotaomicron that are present in many people in abundant levels, and can stably colonize the gut for long periods of time,” Lu says. The team developed a series of genetic parts that can be used to precisely program gene expression within the bacteria. “Using these parts, we built four sensors that can be encoded in the bacterium’s DNA that respond to a signal to switch genes on and off inside B. thetaiotaomicron,” Voigt says. These can be food additives, including sugars, which allow the bacteria to be controlled by the food that is eaten by the host, Voigt adds.
To sense and report on pathologies in the gut, including signs of bleeding or inflammation, the bacteria will need to remember this information and report it externally. To enable them to do this, the researchers equipped B. thetaiotaomicron with a form of genetic memory. They used a class of proteins known as recombinases, which can record information into bacterial DNA by recognizing specific DNA addresses and inverting their direction.
The researchers also implemented a technology known as CRISPR interference, which can be used to control which genes are turned on or off in the bacterium. The researchers used it to modulate the ability of B. thetaiotaomicron to consume a specific nutrient and to resist being killed by an antimicrobial molecule.
The researchers demonstrated that their set of genetic tools and switches functioned within B. thetaiotaomicron colonizing the gut of mice. When the mice were fed food containing the right ingredients, they showed that the bacteria could remember what the mice ate.
Leonid Gurevich, Associate Professor at Aalborg University’s Department of Physics and Nanotechnology, has been involved in the successful experiments conducted by an international research consortium after years of focused collaboration to make molecular electronics a possible replacement for traditional solutions in our ever-smaller devices. In order to provide sufficient processing power in new computers, mobile phones and tablets, the industry is already producing chips today with billions of transistors squeezed into less than one square centimeter: We are now talking about transistors that are less than a thousand atoms across and fabricated with an accuracy of just tens of atoms. And that is just one of the reasons we are rapidly approaching the fundamental limit of conventional semiconductor electronics. A way out could be to mimic nature’s approach — use custom-designed molecules and let them self-assemble into functional devices, explains Leonid Gurevich.
The computer chip in the new iPhone 6 is a good example of what the electronics industry has achieved with constant miniaturization. The chip has two billion transistors and is produced with a resolution of 20 nanometers – just 0.00002 millimeters. Chip manufacturers are even on their way to producing chips with just 14 nanometers resolution. But miniaturization with conventional semiconductor electronics is approaching its fundamental limits, and molecular electronics can be the next step: The idea of molecules replacing electronic circuitry originated back in the 1970s, but it hasn’t left the laboratory yet and development in this field remains largely limited to very short molecules or molecular layers, while conclusive results on long molecules have yet to be obtained. With this paper we establish that charge transport through long molecules is possible, we describe a way to measure single molecules, and we identify the mechanism of charge transport in one of the most promising conductive molecules – G4 DNA. What is certain is that this finding will reinvigorate the field of molecular electronics, in particular DNA electronics, says Leonid Gurevich.
Of course, we cannot expect that DNA will replace silicon in our phones and computers tomorrow. The transition to DNA-based devices or molecular electronics in general will represent a paradigm shift in the way we design, assemble and program electronic devices today. It will be a long journey and we have many questions that we need to answer before DNA electronics becomes a reality. So it’s still too early to say when they will be part of our everyday life. But with sufficient funding, I believe the future looks bright, says Leonid Gurevich.
iSGTW recently interviewed Ewan Birney, associate director of the European Bioinformatics Institute (EBI), regarding his keynote talk at the EUDAT 2nd Conference. In this interview, Birney raised the exciting prospect of using DNA as an organic data storage device. But could DNA storage really replace tapes and hard disks for long-term preservation of data?
Deinococcus radiodurans, for example, a bacterial species adapted to survive in extreme conditions, was chosen by Wong and his colleagues to be the host due to its ability to quickly repair spontaneously arising mutations. Unlike hard drives or magnetic tapes, which are vulnerable to physical damage, data stored in bacteria could survive numerous natural disasters and be safely passed on to future generations.
However, the heterologous (artificially inserted) DNA could make the bacterial genome unstable, believesGeoff Baldwin, reader in biochemistry at Imperial College London, UK. “Bacteria are highly evolved organisms with relatively minimal genomes”, says Baldwin. “There is always the issue that maintaining large quantities of heterologous DNA will exert a fitness burden that will favor loss of the additional DNA, which does not bode well for the use of bacteria as a mass data storage device.”
While mutation rates are relatively low (approximately 1 base every 10,000 generations), bacteria's fast replication rate could make long-term data storage problematic. Another issue is that if the inserted DNA is similar to that of the host bacteria, it could interfere with its normal cellular processes. “Ultimately this means that there is not complete freedom to insert any sequence,” says Baldwin. “This can be overcome to some extent depending on the method chosen to encode information, but there always remains the possibility for instability due to unexpected consequences.”
While data storage in bacteria isn't yet sufficiently developed to be used for mass storage, using ‘naked’ DNA could be a more promising alternative. Mammoths and Neanderthals have been found preserved for thousands of years with DNA sequences intact, showing that living cells are not required for DNA itself to remain an efficient, stable data-storage means. Naked DNA is easier to use than bacteria as it doesn't require genetic manipulations to safely insert it into a host.
Birney was part of a team that encoded a record-breaking 700 kilobytes of unique data — including all 154 of Shakespeare's sonnets — into naked DNA and retrieved it with 99% accuracy. Translating binary data into a 4-base system often results in long sequences of identical bases, which have a tendency to be misread by DNA-sequencing machines, ultimately degrading the information of the original message. The team came up with an ingenious system that allowed them to encode data with such high fidelity. They used a base-3 encoding system: depending on which base was last encoded, a 0, 1 or 2 would correspond with one of the 3 other bases, ensuring the creation of a sequence void of any repetition.
Despite the current high cost of writing and reading DNA, Christophe Dessimoz, another member of the research team, remains hopeful as to the future use of data storage in DNA. “Our analysis shows that for small quantities of data, it is already economically viable for very long term (1,000 years or more), which is relevant for applications such as storing the location of nuclear sites,” says Dessimoz, who is now based atUniversity College London, UK. “If the current pace of technological development continues, within the next decade DNA-based storage will become economical for applications with time horizons of 50 years or more.”
Via Integrated DNA Technologies
DNA SEQ Inc. is both a free-standing, independent and privately held enterprise as well as the centerpiece of a cluster of strategic relationships with other independent organizations which it calls the DNA-SEQ Alliance. Co-founded by noted crystallographer Janusz M. Sowadski, DNA SEQ is headquartered in La Jolla, California. DNA SEQ’s business model for its stand-alone company is two-fold: first, via its website, DNA SEQ will promote the use of its collaborative process to provide for cancer patients and their oncologists an alternative data-driven view of the nature of their disease and possible protein kinase inhibitor molecules for the oncologist to consider prescribing in the course of his or her treatment of the patient. Second, the Company will focus on drug discovery of anti-relapse drugs to fight the recurrence of cancer once initial first-in-line drugs begin to fail, which is a demonstrated and expected phenomenon. DNA SEQ Inc. has taken the crucial and missing steps to make Next Generation diagnostics and treatments a reality sooner rather than later by creating a solid inter-disciplinary and cross-organization collaborative alliance with best-in-class researchers, equipped with cutting edge tools. DNA SEQ will have clients deliver a tissue sample from the pathology laboratory of the hospital where their cancerous tumors were removed directly to the Baylor College of Medicine for the best tissue sample preparation to ready the sample for genomic sequencing. DNA SEQ will then have the option of using Baylor for genomic sequencing and follow-on annotation and analytics, or it can turn to its alternative source of supply,Illumina for Next Generation Sequencing, and on to its joint venture partner Diagnomics for annotation and analytics of the data obtained from Next Generation Sequencing. This most advanced genome annotation and analysis platform will allow DNA SEQ to identify rapidly and very accurately the differences between healthy cells and cancerous cells across the entire functional human genome. Next DNA SEQ will internally construct crystallographic models of the mutated cancer cells and use the models to identify corresponding kinase inhibiting molecules from the more than 120,000 kinase inhibitors currently in existence. At the same time, DNA SEQ will rely on its collaboration with founding shareholder D-Wave Systems Inc. which offers the world’s first Quantum Computing platform to speed up the process of identifying effective protein kinase inhibitor molecules, including FDA-approved drugs and molecules in clinical trials, to cause the “inhibition” or cessation of the rapid division of cells caused by cancerous mutations. Moreover, DNA SEQ will harness D-Wave’s Quantum Computing power to target FDA-approved drugs, and kinase inhibitor in clinical trials, to fight the relapse of cancer once initial first-in-line drugs begin to fail.
The names of more than 100 scientists who will be working to shape a new open-access journal backed by some of the world’s biggest non-governmental research funders have been unveiled. In total, the board of reviewing editors for the eLife journal number 175 and cover biological areas ranging from stem cells to plants to infectious diseases.
A SUITE of artificial intelligence algorithms may become the ultimate chemistry set. Software can now quickly predict a property of molecules from their theoretical structure. Similar advances should allow chemists to design new molecules on computers instead of by lengthy trial-and-error. http://tinyurl.com/7t8usxm
For the first time ever, scientists are using computers and genomic information to predict new uses for existing medicines. “Bringing a new drug to market typically takes about $1 billion, and many years of research and development,” said Rochelle M. Long, Ph.D., who directs the NIH Pharmacogenomics Research Network. “If we can find ways to repurpose drugs that are already approved, we could improve treatments and save both time and money.”
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CROPSR is the first open source software tool for genome-wide design and evaluation of guide RNA (gRNA) sequences for CRISPR experiments, created by scientists at CABBI, a Department of Energy-funded Bioenergy Research Center (BRC). The genome-wide approach significantly shortens the time needed to design a CRISPR experiment, reducing the challenge of working with crops and speeding up the design, evaluation and validation of gRNA sequences, according to the study published in BMC Bioinformatics. To better meet the needs of crop geneticists, the team built software that lifts restrictions imposed by other packages on the design and evaluation of gRNA sequences, the guides used to locate targeted genetic material. Team members also developed a new machine learning model that would not avoid guides for repetitive genomic regions often found in plants, a problem with existing tools. The CROPSR scoring model provided much more accurate predictions, even in non-crop genomes. In the future, he hopes researchers will record their failures as well as their successes to help generate the data needed to train a non-specific model.