Harnessing the power of genomics to find risk factors for major diseases or search for relatives relies on the costly and time-consuming ability to analyze huge numbers of genomes. A team co-led by a Johns Hopkins University computer scientist has leveled the playing field by creating a cloud-based platform that grants genomics researchers easy access to one of the world’s largest genomics databases.

Known as AnVIL (Genomic Data Science Analysis, Visualization, and Informatics Lab-space), the new platform gives any researcher with an Internet connection access to thousands of analysis tools, patient records, and more than 300,000 genomes. The work, a project of the National Human Genome Institute (NHGRI), appears today in Cell Genomics.

“AnVIL is inverting the model of genomics data sharing, offering unprecedented new opportunities for science by connecting researchers and datasets in new ways and promising to enable exciting new discoveries,” said project co-leader Michael Schatz, Bloomberg Distinguished Professor of Computer Science and Biology at Johns Hopkins.

Typically genomic analysis starts with researchers downloading massive amounts of data from centralized warehouses to their own data centers, a process that is not only time-consuming, inefficient, and expensive, but also makes collaborating with researchers at other institutions difficult.

“AnVIL will be transformative for institutions of all sizes, especially smaller institutions that don’t have the resources to build their own data centers. It is our hope that AnVIL levels the playing field so that everyone has equal access to make discoveries,” Schatz said.

Genetic risk factors for ailments such as cancer or cardiovascular disease are often very subtle, requiring researchers to analyze thousands of patients’ genomes to discover new associations. The raw data for a single human genome comprises about 40GB, so downloading thousands of genomes can take takes several days to several weeks: A single genome requires about 10 DVDs worth of data, so transferring thousands means moving “tens of thousands of DVDs worth of data,” Schatz said.  

In addition, many studies require integrating data collected at multiple institutions, which means each institution must download its copy while ensuring that patient-data security is maintained. This challenge is expected to become even greater in the future, as researchers embark on ever-larger studies requiring the analysis of hundreds of thousands to millions of genomes at once.

“Connecting to AnVIL remotely eliminates the need for these massive downloads and saves on the overhead,” Schatz says. “Instead of painfully moving data to researchers, we allow researchers to effortlessly move to the data in the cloud. It also makes sharing datasets much easier so that data can be connected in new ways to find new associations, and it simplifies a lot of computing issues, like providing strong encryption and privacy for patient datasets.”

AnVIL also provides researchers with several major analysis tools, including Galaxy, developed in part at Johns Hopkins, along with other popular tools such as R/Bioconductor, Jupyter notebooks, WDLs, Gen3, and Dockstore to support both interactive analysis and large-scale batch computing. Collectively, these tools allow researchers to tackle even the largest studies without having to build out their computing environments.

Researchers from all over the world currently use the platform to study a variety of genetic diseases, including autism spectrum disorders, cardiovascular disease, and epilepsy. Schatz’s team, part of the Telomere-to-Telomere Consortium, used it to reanalyze thousands of human genomes with the new reference genome to discover more than 1 million new variants.

Already, the AnVIL team has collected petabytes of data from several of the largest NHGRI projects, including hundreds of thousands of genomes from the Genotype-Tissue Expression (GTEx), Centers for Mendelian Genetics (CMG), and Centers for Common Disease Genomics (CCDG) projects, with plans to host many more projects soon.

Samsung Electronics has announced its demonstration of the world's first in-memory computing based on MRAM (Magnetoresistive Random Access Memory). This research showcases Samsung’s leadership in-memory technology and its effort to merge memory and system semiconductors for next-generation artificial intelligence (AI) chips.

The research was led by Samsung Advanced Institute of Technology (SAIT) in close collaboration with Samsung Electronics Foundry Business and Semiconductor R&D Center. The researcher, Dr. Seungchul Jung, Staff Researcher at SAIT, and the co-corresponding scientists Dr. Donhee Ham, Fellow of SAIT and Professor of Harvard University, and Dr. Sang Joon Kim, Vice President of Technology at SAIT, spearheaded the research.

In the standard computer architecture, data is stored in memory chips and data computing is executed in separate processor chips. In contrast, in-memory computing is a new computing paradigm that seeks to perform both data storage and data computing in a memory network. Since this scheme can process a large amount of data stored within the memory network itself without having to move the data, and also because the data processing in the memory network is executed in a highly parallel manner, power consumption is substantially reduced. In-memory computing has thus emerged as one of the promising technologies to realize next-generation low-power AI semiconductor chips.

For this reason, research on in-memory computing has been intensely pursued worldwide. Non-volatile memories, in particular RRAM (Resistive Random Access Memory) and PRAM (Phase-change Random Access Memory), have been actively used for demonstrating in-memory computing. By contrast, it has so far been difficult to use MRAM ─ another type of non-volatile memory ─ for in-memory computing despite MRAM’s merits such as operation speed, endurance, and large-scale production. This difficulty stems from the low resistance of MRAM, due to which MRAM cannot enjoy the power reduction advantage when used in the standard in-memory computing architecture.

The Samsung Electronics researchers have provided a solution to this issue by an architectural innovation. Concretely, they succeeded in developing an MRAM array chip that demonstrates in-memory computing, by replacing the standard, current-sum in-memory computing architecture with a new, ‘resistance sum’ in-memory computing architecture, which addresses the problem of small resistances of individual MRAM devices.

Samsung’s research team subsequently tested the performance of this MRAM in-memory computing chip by running it to perform AI computing. The chip achieved an accuracy of 98% in the classification of hand-written digits and a 93% accuracy in detecting faces from scenes.

By ushering MRAM ─ the memory which has already reached commercial-scale production embedded in the system semiconductor fabrication ─ into the realm of in-memory computing, this work expands the frontier of the next-generation low-power AI chip technologies.

The researchers have also suggested that not only can this new MRAM chip be used for in-memory computing, but it also can serve as a platform to download biological neuronal networks. This is along the line of the neuromorphic electronics vision that Samsung’s researchers recently put forward in a perspective paper.

"In-memory computing draws similarity to the brain in the sense that in the brain, computing also occurs within the network of biological memories, or synapses, the points where neurons touch one another,” said Dr. Seungchul Jung, the first author of the paper. “While the computing performed by our MRAM network, for now, has a different purpose from the computing performed by the brain, such solid-state memory network may in the future be used as a platform to mimic the brain by modeling the brain’s synapse connectivity."

As highlighted in this work, by building on its leading memory technology and merging it with system semiconductor technology, Samsung plans to continue to expand its leadership in next-generation supercomputing and AI semiconductors.

The upcoming satellite experiment LiteBIRD is expected to probe the physics of the very early Universe if the primordial inflation happened at high energies. But now, new research shows it can also test inflationary scenarios operating at lower energies. 

Cosmologists believe that in its very early stages, the Universe underwent a very rapid expansion called “cosmic inflation”. A success story of this hypothesis is that even the simplest inflationary models can accurately predict the inhomogeneous distribution of matter in the Universe. During inflation, these vacuum fluctuations were stretched to astronomical scales, becoming the source of all the structures in the Universe, including the Cosmic Microwave Background anisotropies, distribution of dark matter, and galaxies.

The same mechanism also produced gravitational waves. These propagating ripples of space and time are important for understanding the physics during the inflationary epoch. In general, detecting these gravitational waves is considered to determine the energy at which inflation took place. It is also linked to how much the inflation field, or the energy source of inflation, can change during inflation — a relation referred to as the “Lyth bound”.

The primordial gravitational waves generated from vacuum are extremely weak and are very difficult to detect, but the Japanese-led LiteBIRD mission might be able to detect them via the polarization measurements of the Cosmic Microwave Background. Because of this, understanding primordial gravitational waves theoretically is gaining interest so any potential detection by LiteBIRD can be interpreted. It is expected LiteBIRD will be able to detect primordial gravitational waves if inflation happened at sufficiently high energies.

Several inflationary models constructed in the framework of quantum gravity often predict a very low energy scale for inflation, and so would be untestable by LiteBIRD. However, a new study by researchers, including the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), has shown the opposite. The researchers argue such scenarios of fundamental importance can be tested by LiteBIRD if they are accompanied by additional fields, sourcing gravitational waves.

The researchers suggest an idea, logically very different from the usual.

"Within our framework in addition to the gravitational waves originating from vacuum fluctuations, a large amount of gravitational waves can be sourced by the quantum vacuum fluctuations of additional fields during inflation. Due to this, we were able to produce an observable amount of gravitational waves even if inflation takes place at lower energies.

“The quantum fluctuations of scalar fields during inflation are typically small, and such induced gravitational waves are not relevant in standard inflationary scenarios. However, if the fluctuations of the additional fields are enhanced, they can source a significant amount of gravitational waves,” said paper author and Kavli IPMU Project Researcher Valeri Vardanyan.

Other researchers have been working on related ideas, but so far no successful mechanism based on scalar fields alone had been found.

“The main problem is that when you generate gravitational waves from enhanced fluctuations of additional fields, you also simultaneously generate extra curvature fluctuations, which would make the Universe appear more clumpy than it is in reality. We elegantly decoupled the generation of the two types of fluctuations, and solved this problem,” said Vardanyan.

In their work, the researchers proposed a proof-of-concept based on two scalar fields operating during inflation.

"Imagine a car with two engines, corresponding to the two fields of our model. One of the engines is connected to the wheels of the car, while the other one is not. The first one is responsible for moving the car, and, when on a muddy road, for generating all the traces on the road. These represent the seeds of structure in the Universe. The second engine is only producing sound. This represents the gravitational waves, and does not contribute to the movement of the car, or the generation of traces on the road,” said Vardanyan.

The team quantitatively demonstrated their mechanism works and even calculated the predictions of their model for the upcoming LiteBIRD mission .

An international research team led by scientists at Georgetown University has demonstrated the power of artificial intelligence to predict which viruses could infect humans — like SARS-CoV-2, the virus that led to the COVID-19 pandemic — which animals host them, and where they could emerge. 

Their ensemble of predictive models of likely reservoir hosts was validated in an 18-month project to identify specific bat species likely to carry beta coronaviruses, the group that includes SARS-like viruses.

“If you want to find these viruses, you have to start by profiling their hosts — their ecology, their evolution, even the shape of their wings,” explains the study’s senior author, Colin Carlson, Ph.D., an assistant research professor in the Department of Microbiology & Immunology and a member of Georgetown’s Center for Global Health Science and Security at Georgetown University Medical Center. “Artificial intelligence lets us take data on bats and turn it into concrete predictions: where should we be looking for the next SARS?”

Despite global investments in disease surveillance, it remains difficult to identify and monitor wildlife reservoirs of viruses that could someday infect humans. Statistical models are increasingly being used to prioritize which wildlife species to sample in the field, but the predictions being generated from any one model can be highly uncertain. Scientists also rarely track the success or failure of their predictions after they make them, making it hard to learn and make better models in the future. Together, these limitations mean that there is high uncertainty in which models may be best suited to the task.

This new study suggests that the search for closely-related viruses could be non-trivial, with over 400 bat species around the world predicted to host beta coronaviruses, a large group of viruses that includes those responsible for SARS-CoV (the virus that caused the 2002-2004 outbreak of SARS) and SARS-CoV-2 (the virus that causes COVID-19).  Although the origin of SARS-CoV-2 remains uncertain, the spillover of other viruses from bats is a growing problem due to factors like agricultural expansion and climate change.

Greg Albery, Ph.D., a postdoctoral fellow in Georgetown’s Biology Department, says COVID-19 provided the impetus to expedite their research. “This is a really rare opportunity,” explains Albery. “Outside of a pandemic, we’d never learn this much about these viruses in this small a timeframe. A decade of research has been collapsed into about a year of publications, and it means we can show that these tools work.”

In the first quarter of 2020, the researcher team trained eight different statistical models that predicted which kinds of animals could host beta coronaviruses. Over more than a year, the team then tracked the discovery of 40 new bat hosts of beta coronaviruses to validate initial predictions and dynamically update their models. The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts. In contrast, cutting-edge models from network science that used high-level mathematics – but less biological data – performed roughly as well or worse than expected at random.

“One of the most important things our study gives us is a data-driven shortlist of which bat species should be studied further,” says Daniel Becker, Ph.D., assistant professor of biology at the University of Oklahoma. “After identifying these likely hosts, the next step is then to invest in monitoring to understand where and when beta coronaviruses are likely to spill over.”

Carlson says that the team is now working with other scientists around the world to test bat samples for coronaviruses based on their predictions.

“If we spend less money, resources, and time looking for these viruses, we can put all of those resources into the things that save lives down the road. We can invest in building universal vaccines to target those viruses or monitoring for spillover in people that live near bats,” says Carlson. “It’s a win-win for science and public health.”