CSHL prof Siepel’s team exposes the evolutionary weak spots of the human genome

Mutations can be good and bad. Sometimes they help an organism adapt and survive. Other times they are so harmful that an organism can’t survive or reproduce. Cold Spring Harbor Laboratory (CSHL) Professor Adam Siepel’s team has created a supercomputer program that tracks the history of harmful mutations in the human genome throughout evolution. They discovered parts of the genome are especially vulnerable to mutations, meaning any mutations in those regions can result in severe or lethal consequences. Their findings may help guide clinicians in seeking the origins of serious genetic diseases. Cold Spring Harbor Laboratory Professor Adam Siepel has created a computer program called ExtRaINSIGHT that tracks the history of harmful mutations throughout the evolution of the human genome. He’s discovered regions of the genome that are more vulnerable to mutations than others, and that may be the origins of severe genetic diseases. Image: © ktsdesign – stock.adobe.com

Siepel’s program is called ExtRaINSIGHT. It finds harmful mutations by looking for their absence. By random chance, every part of the human genome should have mutations but some have none. Siepel calls these places “ultra-selected.” When they occur, the mutations there can be deadly or drastically hurt the odds of reproducing. Siepel explains:

“If we look across a panel of a hundred thousand humans and we never see a mutation at a particular gene, that suggests that any mutation that did occur was so harmful, that anyone carrying that mutation died out from the population.”

The team analyzed over 70,000 human genomes with ExtRaINSIGHT. They discovered that three parts of the genome have been extremely sensitive to mutations over generations. Of these, splice sites are the most sensitive. Splice sites help produce correct instructions for making proteins. Mutations here can have a huge impact on the odds of passing down genes, also known as fitness. They’re linked to several diseases including spinal muscular atrophy, the leading genetic cause of death in infants and toddlers. Siepel says:

“If you see a mutation in a splice site, you better take it seriously. That mutation alone would reduce your fitness by 1 or 2%. That doesn’t sound like very much, but that’s a huge fitness effect. And if you had multiple of these, pretty soon your chance of passing on your genes might be close to zero.”

Molecules called miRNA and central nervous system genes are also sensitive. “If you find a mutation in miRNA there’s a good chance it’s responsible for a genetic disease,” Siepel says. “And because the nervous system is so complex and interconnected, it seems particularly sensitive to mutation.”

The origins of many genetic diseases and conditions remain a mystery. Siepel hopes technology like ExtRaINSIGHT will help reveal their origins and guide diagnoses and future treatments. He also hopes his work will help further illustrate how mutations continue to shape the evolution of the human genome.

Schlumberger launches Enterprise Data Solution during the Schlumberger Digital Forum

Customers will make data-driven decisions faster, at scale, through fully integrated cloud-native solutions powered by Microsoft Energy Data Services

At the Schlumberger Digital Forum 2022, which is taking place this week in Lucerne, Switzerland, Schlumberger has announced the commercial release of the Schlumberger Enterprise Data Solution, which is powered by Microsoft Energy Data Services. Developed to deliver the most comprehensive capabilities for subsurface data—in alignment with the emerging requirements of the OSDU Technical Standard, a new open industry standard for energy data—the Enterprise Data Solution makes data accessible on an unprecedented scale for the global energy industry.

This technology enables customers to integrate subsurface data with technologies and workflows from multiple vendors. It is a single, open and interoperable platform with embedded artificial intelligence (AI) and powerful data management tools, which support and accelerate scalable, data-driven decision-making at all levels of the organization.

Microsoft Energy Data Services is a fully managed, enterprise-grade OSDU data platform co-built with domain expertise from Schlumberger, which powers the Enterprise Data Solution. Supported by a global network of specialist development centers around the world including the U.S., India, and Europe, the companies work together to continuously bring new capabilities to market, as well as to provide sales, service, and technical support.

“A global cloud-based data solution, developed by Schlumberger and powered by the Microsoft Cloud, means the energy industry can confidently and fully embrace its digital transformation,” said Rajeev Sonthalia, president, of Digital & Integration, Schlumberger. “Together, Schlumberger’s energy and subsurface data expertise and Microsoft’s experience in scaling cloud-based data solutions in an open interoperable data platform, have successfully unlocked the full potential of data. Accelerating time to value from AI and digital solutions creates significant new opportunities to increase productivity and boost performance. This is the future of data management for the energy industry.”

"A critical aspect of the energy transition process is harnessing data solutions that improve decision making and increase operational efficiency,” said Scott Guthrie, executive vice president, of Cloud + AI Group, Microsoft. “The Schlumberger Enterprise Data Solution, powered by Microsoft Energy Data Services and built on Microsoft Azure, enables organizations in the energy industry to gain greater control of their data and unlock insights that accelerate their journey to data modernization.”

This fully integrated cloud-native enterprise data solution enables end-to-end data-driven workflows scalable to customers’ organizations. Full upstream data capabilities will expand from subsurface to production to well construction and welcome the transition to new and low-carbon energy sources. The Enterprise Data Solution will also accelerate advanced workflows to screen, assess and design carbon capture, utilization, and storage (CCUS) projects to support the rapidly growing demand for large-scale COsequestration. Data previously held in poorly connected silos can now flow freely across an unbroken data landscape to allow AI and automation to work at a previously unimagined scale.

Extraordinary new capabilities and workflows promise to deliver faster and more accurate decision-making, reducing the time it takes for customers to extract additional value from their digitalization strategies.

Early adopters of these exciting new technologies include both PETRONAS and Chevron. PETRONAS has liberated petabytes of E&P data for users, integrating 12 corporate data stores to a single data platform and improving data management efficiency, and optimizing infrastructure. Chevron is working in partnership with Schlumberger and Microsoft to accelerate the creation of digital technologies across its value chain globally.

“Chevron is committed to our collective vision for digital innovation in energy solutions and to working collaboratively to deliver this vision. It is great to see our strategic partners, Microsoft and Schlumberger, embracing the open, industry-data foundation to build innovative products at enterprise scale,” said Kevin Chambers, VP Subsurface, Chevron. “As an early adopter of the OSDU Data Platform, Chevron believes ‘the best is yet to come,’ as we continue to drive innovative capabilities in the OSDU community via our people and industry collaborations and synergies.”

De Paiva compares the gold standard of simulation techniques  with alternative analytics for the use of beta radiation in cancer treatment

A new paper authored by Eduardo De Paiva, from the Division of Medical Physics at the Institute of Radiation Protection and Dosimetry, Rio de Janeiro, Brazil, pits the gold standard simulation method used to calculate the interaction of the ionizing radiation with matter and estimate the radiation dose delivered to a target organ—Monte Carlo (MC) simulation — against an alternative analytic method, the Loevinger formula. A illustration of beta decay proceeding against the backdrop of a Monte Carlo simulation.Credit: Robert Lea

New research pits the simulation of beta radiation doses in tumor treatment against an analytical method.

Treating superficial skin tumors especially when they are located above cartilage or bone with beta radiation can help protect sensitive structures during the delivery of treatment.

The use of short-range beta radiation in cancer treatment is not without its disadvantages, however, especially when it comes to the measurement of radiation exposure — dosimetry. When experimental dosimetry is not feasible, researchers use simulations and calculations to study the interaction of ionizing radiation with matter and estimate the radiation dose delivered to a target organ.

A new paper published in EPJ Plus and authored by Eduardo De Paiva, from the Division of Medical Physics at the Institute of Radiation Protection and Dosimetry, Rio de Janeiro, Brazil, and his colleagues, pits the gold standard of simulation techniques — Monte Carlo (MC) simulation — against an alternative analytic method, the Loevinger formula.

“We measured the dose of a treatment applicator using mathematical techniques — a simple technique, no experiment needed and no practical challenges,” De Paiva says. “The comparison of MC simulation and Loevinger formula on the setup of our research was the novelty of our study.”

Nonexperimental dosimetry techniques like MC simulation are advantageous for their ability to handle different geometries and materials, but MC simulations require heavy computation and this can impede their implementation.

Analytic methods are another set of techniques for dosimetry of beta radiation that can produce results faster than MC methods. Thus far, these methods have been less favored because they are associated with lower accuracy.

The team used MC simulation and analytical calculation — the Loevinger formula — for dosimetry of radiation dose from a multiwell skin brachytherapy applicator with two beta sources. The results of the two approaches were compared to see how accurate the analytical method is.

“The Loevinger formula, which is a quick method for dosimetry showed a good agreement with gold standard Monte Carlo methods,” Paiva concluded. “Thus, the Loevinger formula can be used, as the basis of a dosimetry software, for straightforward dosimetry of beta sources in simple geometries.”