Artificial intelligence applied to the genome identifies an unknown human ancestor

By combining deep learning algorithms and statistical methods, investigators from the Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG) and the Institute of Genomics at the University of Tartu have identified, in the genome of Asian individuals, the footprint of a new hominid who cross bred with its ancestors tens of thousands of years ago.

Modern human DNA computational analysis suggests that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with Out of Africa modern humans in Asia. This finding would explain that the hybrid found this summer in the caves of Denisova — the offspring of a Neanderthal mother and a Denisovan father — was not an isolated case, but rather was part of a more general introgression process.

The study, published in Nature Communications, uses deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution.

Humans had descendants with an species that is unknown to us

One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants. However, this concept is much more complex when extinct species are involved. In fact, the story told by current human DNA blurs the lines of these limits, preserving fragments of hominids from other species, such as the Neanderthals and the Denisovans, who coexisted with modern humans more than 40,000 years ago in Eurasia.

Now, investigators of the Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG), and the University of Tartu have used deep learning algorithms to identify a new and hitherto-unknown ancestor of humans that would have interbred with modern humans tens of thousands of years ago. “About 80,000 years ago, the so-called Out of Africa occurred, when part of the human population, which already consisted of modern humans, abandoned the African continent and migrated to other continents, giving rise to all the current populations,” explained Jaume Bertranpetit, principal investigator at the IBE and head of Department at the UPF. “We know that from that time onwards, modern humans cross bred with Neanderthals in all the continents, except Africa, and with the Denisovans in Oceania and probably in South-East Asia, although the evidence of cross-breeding with a third extinct species had not been confirmed with any certainty.”

Deep learning: deciphering the keys to human evolution in ancient DNA

Hitherto, the existence of the third ancestor was only a theory that would explain the origin of some fragments of the current human genome (part of the team involved in this study had already posed the existence of the extinct hominid in a previous study). However, deep learning has made it possible to make the transition from DNA to the demographics of ancestral populations.

The problem the investigators had to contend with is that the demographic models they have analysed are much more complex than anything else considered to date and there were no statistic tools available to analyse them. Deep learning “is an algorithm that imitates the way in which the nervous system of mammals works, with different artificial neurons that specialise and learn to detect, in data, patterns that are important for performing a given task,” stated Òscar Lao, principal investigator at the CNAG-CRG and an expert in this type of simulations. “We have used this property to get the algorithm to learn to predict human demographics using genomes obtained through hundreds of thousands of simulations. Whenever we run a simulation we are travelling along a possible path in the history of humankind. Of all simulations, deep learning allows us to observe what makes the ancestral puzzle fit together.”

It is the first time that deep learning has been used successfully to explain human history, paving the way for this technology to be applied in other questions in biology, genomics and evolution.

An extinct hominid could explain the history of humankind

The deep learning analysis has revealed that the extinct hominid is probably a descendant of the Neanderthal and Denisovan populations. The discovery of a fossil with these characteristics this summer would seem to endorse the study finding, consolidating the hypothesis of this third species or population that coexisted with modern human beings and mated with them. “Our theory coincides with the hybrid specimen discovered recently in Denisova, although as yet we cannot rule out other possibilities,” said Mayukh Mondal, an investigator of the University of Tartu and former investigator at the IBE.

Quantum computing at scale: Scientists achieve compact, sensitive qubit readout

Professor Michelle Simmons’ team at UNSW Sydney has demonstrated a compact sensor for accessing information stored in the electrons of individual atoms — a breakthrough that brings us one step closer to scalable quantum computing in silicon.

The research, conducted within the Simmons group at the Centre of Excellence for Quantum Computation and Communication Technology (CQC2T) with PhD student Prasanna Pakkiam as lead author, was published today in the journal Physical Review X(PRX).

Quantum bits (or qubits) made from electrons hosted on single atoms in semiconductors is a promising platform for large-scale quantum computers, thanks to their long-lasting stability. Creating qubits by precisely positioning and encapsulating individual phosphorus atoms within a silicon chip is a unique Australian approach that Simmons’ team has been leading globally.

But adding in all the connections and gates required for scale up of the phosphorus atom architecture was going to be a challenge — until now.

“To monitor even one qubit, you have to build multiple connections and gates around individual atoms, where there is not a lot of room,” says Professor Simmons. “What’s more, you need high-quality qubits in close proximity so they can talk to each other — which is only achievable if you’ve got as little gate infrastructure around them as possible.”

Compared with other approaches for making a quantum computer, Simmons’ system already had a relatively low gate density. Yet conventional measurement still required at least 4 gates per qubit: 1 to control it and 3 to read it.

By integrating the read-out sensor into one of the control gates the team at UNSW has been able to drop this to just two gates: 1 for control and 1 for reading.

“Not only is our system more compact, but by integrating a superconducting circuit attached to the gate we now have the sensitivity to determine the quantum state of the qubit by measuring whether an electron moves between two neighbouring atoms,” lead author Pakkiam states.

“And we’ve shown that we can do this real-time with just one measurement — single shot — without the need to repeat the experiment and average the outcomes.”

“This represents a major advance in how we read information embedded in our qubits,” concludes Simmons. “The result confirms that single-gate reading of qubits is now reaching the sensitivity needed to perform the necessary quantum error correction for a scalable quantum computer.”

Australia’s first quantum computing company

Since May 2017, Australia’s first quantum computing company, Silicon Quantum Computing Pty Limited (SQC), has been working to create and commercialise a quantum computer based on a suite of intellectual property developed at the Australian Centre of Excellence for Quantum Computation and Communication Technology (CQC2T).

Co-located with CQC2T on the UNSW Campus in Sydney, SQC is investing in a portfolio of parallel technology development projects led by world-leading quantum researchers, including Australian of the Year and Laureate Professor Michelle Simmons. Its goal is to produce a 10-qubit demonstration device in silicon by 2022 as the forerunner to a commercial scale silicon-based quantum computer.

SQC believes that quantum computing will ultimately have a significant impact across the global economy, with possible applications in software design, machine learning, scheduling and logistical planning, financial analysis, stock market modelling, software and hardware verification, climate modelling, rapid drug design and testing, and early disease detection and prevention.

Created via a unique coalition of governments, corporations and universities, SQC is competing with some of the largest tech multinationals and foreign research laboratories.

As well as developing its own proprietary technology and intellectual property, SQC will continue to work with CQC2T and other participants in the Australian and International Quantum Computing ecosystems, to build and develop a silicon quantum computing industry in Australia and, ultimately, to bring its products and services to global markets.

Guidelines for a standardized data format for use in cross-linguistic studies

Since designing and launching a specialized workflow management system in 2010, a research team from the US Department of Energy’s Oak Ridge National Laboratory has continuously updated the technology to help computational scientists develop software, visualize data and solve problems.

Workflow management systems allow users to prepare, produce and analyze scientific processes to help simplify complex simulations. Known as the Eclipse Integrated Computational Environment, or ICE, this particular system incorporates a comprehensive suite of scientific computing tools designed to save time and effort expended during modeling and simulation experiments.

Compiling these resources into a single platform both improves the overall user experience and expedites scientific breakthroughs. Using ICE, software developers, engineers, scientists and programmers can define problems, run simulations locally on personal computers or remotely on other systems — even supercomputers — and then analyze results and archive data. Recently, the team published an article in SoftwareX that both details the history of the system and previews the potential benefits of upcoming versions.

“What I really love about this project is making complicated computational science automatic,” said Jay Jay Billings, a researcher in ORNL’s Computer Science and Mathematics Division who leads the ICE development team. “Building workflow management systems and automation tools is a type of futurism, and it’s challenging and rewarding to operate at the edge of what’s possible.”

Researchers use ICE to study topics in fields including nuclear energy, astrophysics, additive manufacturing, advanced materials, neutron science and quantum computing, answering questions such as how batteries behave and how some 3D-printed parts deform when exposed to heat.

Several factors differentiate ICE from other workflow management systems. For example, because ICE exists on an open-source software framework called the Eclipse Rich Client Platform, anyone can access, download and use it. Users also can create custom combinations of reusable resources and deploy simulation environments tailored to tackle specific research challenges.

“Eclipse ICE is an excellent example of how open-source software can be leveraged to accelerate science and discovery, especially in scientific computing,” said Eclipse Foundation Executive Director Mike Milinkovich. “The Eclipse Foundation, through its community-led Science Working Group, is fostering open-source solutions for advanced research in all areas of science.”

Additionally, ICE circumvents the steep and time-consuming learning curve that usually accompanies any computational science project. Although other systems require expert knowledge of the code and computer in question, ICE enables users to immediately begin facilitating their experiments, thus helping them gather data and achieve results much faster.

“We’ve produced a streamlined interface to computational workflows that differs from complicated systems that you have to be specifically qualified in to use properly,” Billings said.

Throughout this project, Billings has also emphasized the importance of accessibility and usability to ensure that users of all ages and experience levels, including nonscientists, can use the system without prior training.

“The problem with a lot of workflow management systems and with modeling and simulation codes in general is that they are usually unusable to the lay person,” Billings said. “We designed ICE to be usable and accessible so anyone can pick up an existing code and use it to address pressing computational science problems.”

ICE uses the programming language Java to define workflows, whereas other systems use more obscure languages. Thus, students in grade school, high school and college have successfully run codes using ICE.

Finally, instead of relying on grid workflows — collections of orchestrated computing processes — ICE focuses on flexible modeling and simulation workflows that give users interactive control over their projects. Grid workflows are defined by strict parameters and executed without human intervention, but ICE allows users to input additional information during simulations to produce more complicated scenarios.

“In ICE you can have humans in the loop, meaning the program can stop, ask questions and receive instructions before resuming activity,” Billings said. “This feature allows system users to complete more complex tasks like looping and conditional branching.”

Next, the development team intends to combine the most practical aspects of ICE and other systems through workflow interoperability, a concept referring to the ability of two different systems to seamlessly communicate. Combining the best features of grid workflows with modeling and simulation workflows would allow scientists to address even greater challenges and solve scientific mysteries more efficiently.

“If I’m using ICE and someone else is using a different system, we want to be able to address problems together with our combined resources,” Billings said. “With workflow interoperability, our systems would have a standard method of ‘talking’ to one another.”

To further improve ICE’s accessibility and usability, the team is also developing a cloud-based version to provide even more interactive computing services for simplifying scientific workflows.

“That’s what research is — we keep figuring out the next step to understand the system better,” Billings said.

Tech streamlines computational science projects

Since designing and launching a specialized workflow management system in 2010, a research team from the US Department of Energy’s Oak Ridge National Laboratory has continuously updated the technology to help computational scientists develop software, visualize data and solve problems.

Workflow management systems allow users to prepare, produce and analyze scientific processes to help simplify complex simulations. Known as the Eclipse Integrated Computational Environment, or ICE, this particular system incorporates a comprehensive suite of scientific computing tools designed to save time and effort expended during modeling and simulation experiments.

Compiling these resources into a single platform both improves the overall user experience and expedites scientific breakthroughs. Using ICE, software developers, engineers, scientists and programmers can define problems, run simulations locally on personal computers or remotely on other systems — even supercomputers — and then analyze results and archive data. Recently, the team published an article in SoftwareX that both details the history of the system and previews the potential benefits of upcoming versions.

“What I really love about this project is making complicated computational science automatic,” said Jay Jay Billings, a researcher in ORNL’s Computer Science and Mathematics Division who leads the ICE development team. “Building workflow management systems and automation tools is a type of futurism, and it’s challenging and rewarding to operate at the edge of what’s possible.”

Researchers use ICE to study topics in fields including nuclear energy, astrophysics, additive manufacturing, advanced materials, neutron science and quantum computing, answering questions such as how batteries behave and how some 3D-printed parts deform when exposed to heat.

Several factors differentiate ICE from other workflow management systems. For example, because ICE exists on an open-source software framework called the Eclipse Rich Client Platform, anyone can access, download and use it. Users also can create custom combinations of reusable resources and deploy simulation environments tailored to tackle specific research challenges.

“Eclipse ICE is an excellent example of how open-source software can be leveraged to accelerate science and discovery, especially in scientific computing,” said Eclipse Foundation Executive Director Mike Milinkovich. “The Eclipse Foundation, through its community-led Science Working Group, is fostering open-source solutions for advanced research in all areas of science.”

Additionally, ICE circumvents the steep and time-consuming learning curve that usually accompanies any computational science project. Although other systems require expert knowledge of the code and computer in question, ICE enables users to immediately begin facilitating their experiments, thus helping them gather data and achieve results much faster.

“We’ve produced a streamlined interface to computational workflows that differs from complicated systems that you have to be specifically qualified in to use properly,” Billings said.

Throughout this project, Billings has also emphasized the importance of accessibility and usability to ensure that users of all ages and experience levels, including nonscientists, can use the system without prior training.

“The problem with a lot of workflow management systems and with modeling and simulation codes in general is that they are usually unusable to the lay person,” Billings said. “We designed ICE to be usable and accessible so anyone can pick up an existing code and use it to address pressing computational science problems.”

ICE uses the programming language Java to define workflows, whereas other systems use more obscure languages. Thus, students in grade school, high school and college have successfully run codes using ICE.

Finally, instead of relying on grid workflows — collections of orchestrated computing processes — ICE focuses on flexible modeling and simulation workflows that give users interactive control over their projects. Grid workflows are defined by strict parameters and executed without human intervention, but ICE allows users to input additional information during simulations to produce more complicated scenarios.

“In ICE you can have humans in the loop, meaning the program can stop, ask questions and receive instructions before resuming activity,” Billings said. “This feature allows system users to complete more complex tasks like looping and conditional branching.”

Next, the development team intends to combine the most practical aspects of ICE and other systems through workflow interoperability, a concept referring to the ability of two different systems to seamlessly communicate. Combining the best features of grid workflows with modeling and simulation workflows would allow scientists to address even greater challenges and solve scientific mysteries more efficiently.

“If I’m using ICE and someone else is using a different system, we want to be able to address problems together with our combined resources,” Billings said. “With workflow interoperability, our systems would have a standard method of ‘talking’ to one another.”

To further improve ICE’s accessibility and usability, the team is also developing a cloud-based version to provide even more interactive computing services for simplifying scientific workflows.

“That’s what research is — we keep figuring out the next step to understand the system better,” Billings said.