Category Archives: Transhuman News

News Release

Friday, October 18, 2019

HudsonAlpha awarded $7 million to expand national health dataset with uncharted genetic variants.

The All of Us Research Program has selected the HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, to evaluate the use of leading-edge DNA sequencing technologies that could someday improve diagnosis and treatment of many diseases, both common and rare. The National Center for Advancing Translational Sciences (NCATS) is funding the project with $7 million over one year. All of Us and NCATS are parts of the National Institutes of Health.

All of Us will provide one of the worlds most robust platforms for precision medicine research, with a broad range of data to drive new discoveries, said Eric Dishman, All of Us director. Through this partnership with NCATS, well be able to offer approved researchers an even greater depth of genetic information than originally planned, making the resource even more valuable for them and the diverse communities we seek to help.

With this award, HudsonAlpha will use long-read whole genome sequencing technologies to generate genetic data on about 6,000 samples from participants of different backgrounds. Long-read sequencing analyzes DNA in larger segments than standard (short-read) sequencing technologies, exposing genetic variations that may otherwise go undetected. These variations include different types of alterations to the genetic structure, such as duplication, deletion or rearrangement of the building blocks that uniquely make up ones genome and set it apart from others. Everyone has thousands of these genetic variations, most with little known effect. However, researchers are learning more about how some genetic variants underlie certain health conditions or, conversely, increase disease resistance. Understanding the genetic underpinnings of health and disease will help researchers identify more targeted interventions in the future.

This project will allow researchers to better determine the value of long-read sequencing and its strengths and limitations in exploring more elusive parts of the genome. Combined with the 1 million whole genome sequences the program already plans to deliver over the next several years, this additional infusion of genetic information will provide the research community with the largest collection of genomic structural variation data and clinical data ever produced.

Because long-read sequencing can reveal genetic changes associated with rare diseases, this project is an opportunity to assess and potentially refine the technology for advancing research across the many diseases for which there is no treatment, said Christopher P. Austin, M.D., NCATS director. This project illustrates the power of data and technology to accelerate the translation of knowledge into improved health.

The HudsonAlpha team, led by Shawn Levy, Ph.D., brings significant experience in large-scale sequencing projects and in genetic studies on inherited disorders as well as complex conditions, including autism, diabetes, cancer, schizophrenia, degenerative neurological disease and amyotrophic lateral sclerosis(ALS).

We look forward to collaborating with the other All of Us genome centers and the rest of the consortium on this exciting effort, said Dr. Levy. Contributing long-read sequencing data to reveal additional structural variants will enable the scientific community to study human diversity on a tremendous scale.Appreciating the impacts of all types of genetic variation will further unravel the genetic, environmental and behavioral influences of health.

About theAll of UsResearch Program:Themissionof theAll of UsResearch Program is to accelerate health research and medical breakthroughs, enabling individualized prevention, treatment, and care for all of us. The programwill partner with one million or more people across the United States to build the most diverse biomedical data resource of its kind, to help researchers gain better insights into the biological, environmental, and behavioral factors that influence health. For more information, visitwww.JoinAllofUs.organdwww.allofus.nih.gov.

About the National Center for Advancing Translational Sciences (NCATS):NCATS conducts and supports research on the science and operation of translation the process by which interventions to improve health are developed and implemented to allow more treatments to get to more patients more quickly. For more information about how NCATS is improving health through smarter science, visithttps://ncats.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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NIH funds new All of Us Research Program genome center to test advanced sequencing tools - National Institutes of Health

Jefferson Education Exchange will create a framework to help school and district leaders understand why edtech products work in some contexts but not in others

WASHINGTON, Oct. 24, 2019 /PRNewswire/ --A new first-of-its-kind initiative, announced today, will create an evidence-based framework to help educators make better-informed decisions about what technology to use in their classrooms -- and how to implement it most effectively. Coordinated by the nonprofit Jefferson Education Exchange, the EdTech Genome Project will be overseen by a diverse group of educators, association leaders, researchers, and technology experts who represent the most influential voices in the national conversation on education technology.

(PRNewsfoto/Jefferson Education Exchange)

"Teachers are on the frontlines of decision-making about what technology to use in the classroom. Getting useful information to address the needs of students in our specific contexts helps us provide an equitable education," said Alexander Kmicikewycz, a teacher at Chicago Public Schools. "That's why teacher voice is so important in education research -- and why it's so exciting to be part of a research project that is bringing together stakeholders from across the education community to tackle such a significant challenge for educators."

The EdTech Genome Project is designed to address a critical collective action problem in education technology. Each year, educators and school administrators spend more than $13 billion on more than seven thousand technology tools and products. But because most purchasing decisions are chiefly influenced by word of mouth or internet searches, an estimated 85% of edtech tools are either a poor fit for a particular school, or are not implemented effectively. Yet there is no system through which educators can report the results of their implementations so that others may learn from them. As a result, billions of dollars continue to be wasted on tools and products that do not meaningfully improve student outcomes -- despite the best of intentions by all involved.

Backed by philanthropic and social impact organizations including Strada Education Network, the Chan Zuckerberg Initiative, and Carnegie Corporation of New York, the project will draw on extensive research and direct outreach with educators to identify up to ten contextual variables associated with edtech implementation success or failure. Once the stakeholders reach consensus on the list of variables, up to ten national working groups will be formed by bringing together the leading researchers and practitioners with deep experience in each variable. Each working group will then spend a year examining existing evidence and measurement instruments as each group works to reach consensus about how implementation factors such as "teacher agency," "initiative fatigue," "quality of professional development," and other technical and cultural factors can be quantified.

"If you want to improve your health, experts will ask you questions about your diet, current lifestyle, and fitness goals before recommending dietary changes and a fitness routine. For some people, a better choice may be water aerobics or yoga, while others may be ready to train for a marathon. But in education, we haven't applied research the same way," said Katrina Stevens, Director of Learning Sciences for the Chan Zuckerberg Initiative. "This is a critical first step towards creating a shared understanding of what technology works, where, and why."

Story continues

The EdTech Genome Project will be directed by a 30-member steering committee made up of leaders from education and research organizations including ISTE, the National Education Association, the American Federation of Teachers, Gallup, and the American Institutes for Research, as well as teachers and technology leaders from seven public school districts across the country.

"The way we use -- and misuse -- education technology has profound costs in not just economic, but equity, terms," said Bart Epstein, president and CEO of the Jefferson Education Exchange and a research associate professor at the University of Virginia Curry School of Education and Human Development. "This effort is about empowering educators and administrators by providing them access to the hard-earned experiences of their peers nationwide. Better understanding of what works where and why will enable them to fulfill the promise of technology to improve outcomes for all students."

The steering committee for the EdTech Genome Project includes:

About Jefferson Education ExchangeTheJefferson Education Exchange is a nonprofit public charity committed to bringing educator perspectives to bear on edtech procurement and research. Supported by the University of Virginia's Curry School of Education and Human Development, the Jefferson Education Exchange's work centers on research and development to guide the design of research protocols and tools that will enable educators to document and share their experiences with education technology products. Connect with us on Twitter andLinkedIn.

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Ambitious Initiative Brings Together Education Sector to "Map the EdTech Genome" - Yahoo Finance

If you synthesized every variant of a 79-nucleotide long piece of DNA (479), their mass would be greater than the mass of the earth. If you did the same thing, but with 126 nucleotides, their mass would be greater than the observable universe. I first read about these hyper-astronomical numbers in a review by Ard Louis, Professor of Theoretical Physics at the University of Oxford.

Given that the E. coli genome is five million bases long, and the human genome is 600 times longer, how can engineers possibly know which precise modifications are best to overproduce some chemical, or which edits to make to stop the spread of a cancer? Nature has barely begun to explore this coding space, and we might be nave to think that we could do better.

In synthetic biology, the Design-Build-Test cycle is used to create organisms with desired properties. But, given the possible combinations of DNA sequences, it is nearly impossible to design the perfect DNA sequence for our desired application. A growing tide of computer-aided biology promises to accelerate this pipeline, using automated liquid handlers to Build (and in some cases Test), and feeding the resulting data into machine learning algorithms, which Design the next set of experiments.

Machine learning is just a set of algorithms that computers use to perform specific tasks without a set of direct instructions. Feed the computer lots of high-quality data, and it will tell you which experiment to perform next.

The Echo Liquid Handler developed by Labcyte, now part of Beckman Coulter Life Sciences, can help provide that data.

Moving liquid with sound waves, the Echo Liquid Handler is contact-free and capable of transferring 2.5 or 25 nanoliter increments precisely and accurately. For the synthetic biologist, you can combine a range of fluids from oligonucleotides, master mixes with enzymes to lysates for TX-TL expression without the need to calibrate the instrument, says John Lesnick, Senior Scientist at Beckman Coulter. The whole process is also remarkably fast, with the Echo transferring hundreds of droplets per second. This speed and precision enables scientists to test a far greater number of variants than would ever be possible by hand. The data collected here, once fed into machine learning algorithms, can be used to answer challenging questions, like:

Which guideRNA will best edit this gene?

Which promoter should I use to express this protein?

How can I modify this enzyme to maximize its catalytic turnover?

Many high-powered synthetic biology companies are already using this computer-aided approach to rewire biology at breakneck pace. Genome editing company Inscripta used machine learning algorithms to develop an all-in-one platform that can make hundreds of precise genome edits simultaneously in living cells. Zymergen is producing high-performance materials not found anywhere in nature by using machine learning to dictate which genetic modifications to make in an organism.

We are entering an era of computer-aided synthetic biology, where machines can run experiments, analyze the data, and design the next experiments. It may help us explore the darkest corners of genomes, and create incredible chemicals and products once inaccessible to nature.

Given the hyper-astronomical combinations that a DNA sequence can adopt, how can scientists find the best combination for their application?

Consider Inscriptas digital genome engineering platform, which was recently used to create a 200,000-edit library of an E. coli biosynthesis pathway. The system, which is an enclosed device that was formally announced at the SynBioBeta conference, uses CRISPR/Cas9 to make thousands of parallel edits at specific regions of the genome.

Richard Fox, Executive Director of Data Science at Inscripta.

The basic CRISPR technology has at least two key features, says Dr. Richard Fox, Executive Director of Data Science at Inscripta. One is the ability to cut a gene, then paste and repair So its probably not a stretch to imagine that were using data to derive the rules that optimize the editing processes.

To determine which guideRNAs are best for each edit, they leveraged machine learning. We generate a lot of data to figure out which designs work better than others, and part of improving our system performance is to use that data to empirically determine, along with statistics and machine learning, which guides are best for cutting, explains Fox.

But the utility of machine learning doesnt stop there. Inscripta is also using their gene editing platform to inform protein engineering and directed evolution, the same method that earned Frances Arnold, Professor at the California Institute of Technology, the 2018 Nobel Prize in Chemistry.

We spent a fair bit of time working in the field of protein engineering, especially through methods like directed evolution. Specifically, we generated genotype and phenotype data around enzymes and other proteins, says Fox, referring to how a DNA sequence encoding a protein can impact the observable characteristics of an organism (i.e., its phenotype).

Inscripta, for example, can make many different edits in the DNA sequence encoding an enzyme, and then run experiments to measure the phenotypes that result. If the enzyme is responsible for producing a bright pigment, then certain edits will cause it to produce more or less of that pigment. By feeding that information into machine learning pipelines, the algorithm can predict which edits will maximize the desired phenotype.

Forty minutes northwest of Inscripta, in Emeryville, California, Zymergen is using similar strategies to engineer the future of molecules and materials.

Biology is an incredibly powerful, multi-purpose tool, and it can be aimed at any number of different endpoints, says Aaron Kimball, CTO of Zymergen.

Within a 310,000 square-foot space in Emeryville, Zymergen is using engineered organisms to produce a slew of chemicals and materials. Though inspired by nature, many of the materials they create are not found anywhere else on earth. Just like Inscripta, their engineering process requires the exploration of hyper-astronomical spaces to find the DNA combinations that work best to produce a desired compound.

To build the strains that we use for full-scale fermentation, we use lab automation systems. Theres a loop that we usedesign, build, test and analyze. In the design phase, we design many genetic edits that we believe will be beneficial, then we physically make those edits in our strains, and then we test them for different responses, says Kimball.

Once liquid handlers are used to build the strains, the resulting data informs the next round of experiments, says Kimball. In the learning phase, we update any models that we made and feed that information into the next round of designso the build phase and the test phase are, essentially, entirely performed on lab automation systems so that we can perform this work at scale.

In Zymergens case, the objective is to engineer organisms that can produce custom designed materials and chemicals at scale. Often, seemingly innocuous edits in a single gene can lead to superior strains with higher production capabilities. But finding which edits to make requires a constant dialogue between data and algorithms.

We have one of the largest libraries of DNA available in the worldas well as an electronic database that we can search. So we can use machine learning algorithms to search that database for homologs to genes that might be beneficial replacements over the genes that are naturally encoded in an organism, says Kimball. We might also [use machine learning to] search more heavily into the dark matter of the genome, the genes of no known functionand then combine beneficial edits or mutations.

Despite being founded in 2013, Zymergen has already used this automation-meets-machine-learning approach with remarkable success. Although the company hasnt revealed their customers, co-founder and CEO Joshua Hoffman has previously stated that their clients have sold half a billion dollars worth of products made with our bugs in the last couple of years.

Zymergen CEO Joshua Hoffman

Synthetic biologists want to create a biological future that moves our civilization away from petrochemicals, demonstrates the promise of renewables, and produces high quality products for less. Considering the hyper-astronomical possibilities of genetic sequences, it is clear that humans are poorly equipped to test this vast space.

But maybe high-throughput liquid handlers, like the Echo, coupled with machine learning algorithms, can help us learn from biology, sift through the dark corners of genomes, and continue advancing this biologized industrial revolution.

All trademarks are properties of their respective owners.

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How Automation and Machine Learning Help Explore the Dark Corners of the Genome - SynBioBeta

MINNEAPOLIS, Oct. 24, 2019 (GLOBE NEWSWIRE) -- Predictive Oncology (NASDAQ: POAI) (Predictive Oncology or the Company), a company focused on applying artificial intelligence (AI) to personalized medicine and drug discovery, today announces that Helomics CTO Dr. Mark Collins will speak during the scientific session of the conference, and will participate in an invite-only session on the expansion of the UK100K GP to 5 Million genomes.

Helomics partnered with the UK100K GP in November 2018 to utilize whole genome and outcome data from the project to advance its efforts to build predictive models of various cancers. The company is leveraging its proprietary, multi-omic database of tumor drug response profiles using the proven power of AI to build predictive models of ovarian cancer. These AI-driven predictive models will be used by clinicians to individualize treatment options and by pharma researchers to develop new targeted therapies in the quest to improve outcomes for women with ovarian cancer.

This partnership is key to our approach of building AI-driven predictive models. We are delighted to participate in the scientific spotlight session, panel discussion and an invite-only session on the expansion of the UK 100,000 Genomes Project to 5 million genomes, stated Dr. Collins. The depth of both the genomic and clinical data for ovarian cancer from the project is best-in-class, yielding useful benchmarks to validate our models. Over the next year, we intend to explore additional projects to enable use of our AI-driven predictive models to improve outcomes for ovarian cancer patients in the UK, as well as seek partnerships with UK pharma companies for the development of new precision ovarian cancer therapies.

The inaugural UK 100,000 Genomes Project (UK100K GP) conference is hosted by Genomics England, taking place Nov. 4, 2019, in London. UK100K GP is a groundbreaking initiative sequencing whole genomes of National Health Service patients with rare diseases and their families, as well as patients with common cancers. The aim is to transform healthcare through new diagnoses and personalized treatments.

According to a research report by Global Market Insights Inc., the precision medicine market is set to exceed US$96 billion by 2024. Helomics continues to be an innovative precision medicine company positioned for continued growth in this robust market.

About the 100,000 Genomes ProjectThe 100,000 Genomes Project is a UK Government project that is sequencing whole genomes from National Health Service patients. The project is focusing on rare diseases, some common types of cancer and infectious diseases. Recruitment of participants to the 100,000 Genomes Project was completed in 2018, with the 100,000th sequence achieved in December 2018.

Combining genomic sequence data with medical records has created a ground-breaking research resource in the quest to bring advanced diagnosis and personalized treatments to all those who need them. To date, actionable findings have been found for one in four/one in five rare disease patients, and around 50% of cancer cases contain the potential for a therapy or a clinical trial.

About Predictive Oncology

Predictive Oncology (NASDAQ: POAI) is an AI-driven company focused on applying artificial intelligence to personalized medicine and drug discovery. The company applies smart tumor profiling and its AI platform to extensive genomic and biomarker patient data sets to predict clinical outcomes and, most importantly, improve patient outcomes for cancer patients of today and tomorrow.

Predictive Oncology currently has approximately 150,000 clinically validated cases on its molecular information platform, 38,000+ specific to ovarian cancer. The companys data is highly differentiated, having both drug response data and access to historical outcome data from patients. Predictive Oncology intends to generate additional sequence data from these tumor samples to deliver on the clear unmet market need across the pharmaceutical industry for a multi-omic approach to new drug development.

For more information, visit the companys website at http://www.predictive-oncology.com.

Corporate Communications:NetworkWire (NW)New York, New Yorkwww.NetworkNewsWire.com212.418.1217 OfficeEditor@NetworkWire.com

Gerald Vardzel Jr.Helomics Corporation, PresidentA division of Predictive Oncology Inc.91 43rd Street, Suite 110Pittsburgh, Pennsylvania 15201412.432.1508 gvardzel@helomics.com

Forward-Looking Statements

Certain of the matters discussed in the press release contain forward-looking statements that involve material risks to and uncertainties in the Companys business that may cause actual results to differ materially from those anticipated by the statements made herein. Such risks and uncertainties include (i) risks related to the recent merger with Helomics, including the fact that the combined company will not be able to continue operating without additional financing; possible failure to realize anticipated benefits of the merger; costs associated with the merger may be higher than expected; the merger may result in disruption of the Companys and Helomics existing businesses, distraction of management and diversion of resources; and the market price of the Companys common stock may decline as a result of the merger; (ii) risks related to our partnerships with other companies, including the need to negotiate the definitive agreements; possible failure to realize anticipated benefits of these partnerships; and costs of providing funding to our partner companies, which may never be repaid or provide anticipated returns; and (iii) other risks and uncertainties relating to the Company that include, among other things, current negative operating cash flows and a need for additional funding to finance our operating plan; the terms of any further financing, which may be highly dilutive and may include onerous terms; unexpected costs and operating deficits, and lower than expected sales and revenues; sales cycles that can be longer than expected, resulting in delays in projected sales or failure to make such sales; uncertain willingness and ability of customers to adopt new technologies and other factors that may affect further market acceptance, if our product is not accepted by our potential customers, it is unlikely that we will ever become profitable; adverse economic conditions; adverse results of any legal proceedings; the volatility of our operating results and financial condition; inability to attract or retain qualified senior management personnel, including sales and marketing personnel; our ability to establish and maintain the proprietary nature of our technology through the patent process, as well as our ability to possibly license from others patents and patent applications necessary to develop products; Predictive Oncologys ability to implement its long range business plan for various applications of its technology; Predictive Oncologys ability to enter into agreements with any necessary marketing and/or distribution partners and with any strategic or joint venture partners; the impact of competition, the obtaining and maintenance of any necessary regulatory clearances applicable to applications of Predictive Oncologys technology; and management of growth and other risks and uncertainties that may be detailed from time to time in the Companys reports filed with the SEC, which are available for review at http://www.sec.gov. This is not a solicitation to buy or sell securities and does not purport to be an analysis of Predictive Oncologys financial position. See Predictive Oncologys most recent Annual Report on Form 10-K, and subsequent reports and other filings at http://www.sec.gov.

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Predictive Oncology: Helomics to present initial findings from its analysis of UK 100,000 Genomes Project data towards its goal of building AI-driven...

The 1KP initiative, a collaborative effort of nearly 200 scientists, spans green algae to land plants, providing a framework for examining 1 billion years of plant evolution. Credit: Eric Zamora/Florida Museum

Plants are evolutionary champions, dominating Earths ecosystems for more than a billion years and making the planet habitable for countless other life forms, including us. Now, scientists have completed a nine-year genetic quest to shine a light on the long, complex history of land plants and green algae, revealing the plot twists and furious pace of the rise of this super group of organisms.

The project, known as the One Thousand Plant Transcriptomes Initiative (1KP), brought together nearly 200 plant biologists to sequence and analyze genes from more than 1,100 plant species spanning the green tree of life. A summary of the teams findings published on October 23, 2019, in Nature.

In the tree of life, everything is interrelated, said Gane Ka-Shu Wong, lead investigator of 1KP and professor in the University of Albertas department of biological sciences. And if we want to understand how the tree of life works, we need to examine the relationships between species. Thats where genetic sequencing comes in.

Much of plant research has focused on crops and a few model species, obscuring the evolutionary backstory of a clade that is nearly half a million species strong.

To get a birds-eye view of plant evolution, the 1KP team sequenced transcriptomes the set of genes that is actively expressed to illuminate the genetic underpinnings of green algae, mosses, ferns, conifers, flowering plants and all other lineages of green plants.

One hallmark of plant evolution is the frequency of genome duplication. Flowering plants are renowned for making multiple copies of their genome, which may contribute to the evolution of new gene functions. The 1KP project uncovered previously unknown duplication events in this group. Credit: Kristen Grace/Florida Museum

This gives a much broader perspective than what you could get by just looking at crops, which are all concentrated in one little part of the evolutionary tree, said study co-author Pamela Soltis, University of Florida distinguished professor and Florida Museum of Natural History curator. By having this bigger picture, you can understand how changes occurred in the genome, which then allows you to investigate changes in physical characteristics, chemistry or any other feature youre interested in.

One challenge was the projects sheer size, said study co-author Douglas Soltis, UF distinguished professor, and Florida Museum curator.

To look at that many genomes is unparalleled, he said. Its not a jump in technology as much as a jump in scale.

Sequencing transcriptomes requires freshly collected tissues, which is how Soltis found himself trekking through Gainesvilles greenery with containers of liquid nitrogen. Back at the laboratory, a team extracted genetic material from the frozen plant clippings and shipped the extractions to China for sequencing. All over the world, their colleagues followed suit.

Analyzing the sequences also required a reworking of existing software, which wasnt designed to handle such an unprecedented volume of genetic data, and without funding for the analysis, the researchers chipped away at the data as they had spare time.

But the labor was worth it, Pamela Soltis said.

The plant community got more than 1,000 sets of sequences, said Soltis, who also directs the UF Biodiversity Institute. Who could argue with that? All these branches of the plant tree of life have been filled in.

One hallmark of plant evolution and a feature rarely seen in animals is the frequency of genome duplication. Over and over again, lineages doubled, tripled or even quadrupled their entire set of genes, resulting in massive genome sizes. While the purpose of whole genome duplication is still unclear, scientists suspect that it may drive evolutionary innovation: If you have two copies of genes, one copy can gradually evolve a new function.

Addressing the frequency of whole genome duplication in plants was one of 1KPs goals, Douglas Soltis said. While flowering plants and ferns were already famous for genome duplication, Soltis said 1KP uncovered a number of previously unknown duplication events in these groups, as well as in the gymnosperms, the group of plants that includes conifers.

Other plant lineages took a different route, expanding certain gene families rather than copying their entire genome. This, too, is thought to provide new avenues for evolutionary development, and not surprisingly, the research team uncovered a major expansion of genes just before the appearance of vascular plants, land plants with xylem and phloem special cells for transporting water and nutrients.

But Douglas Soltis said gene expansions did not always correspond to major plant evolutionary milestones.

Theres not much of an expansion before seed plants appear or for flowering plants, he said. In fact, flowering plants actually shrank certain gene families, which may be a sign that they just co-opted existing genes for new functions.

Another surprise finding was that mosses, liverworts and hornworts form a single related group, confirming a centuries-old hypothesis that had been reversed in recent decades.

Wed done a partial analysis in 2014 that suggested these plants were close relatives, but a lot of people didnt believe it. These results underscore those findings, Pamela Soltis said. Its going to rock the moss world.

While the project refines our understanding of plant evolution and relationships between lineages, these data are also invaluable tools for advancing crop science, medicine, and other fields, the researchers said.

Identifying genes that have been duplicated in flowering plants could help scientists better understand their function, which could lead to crop improvements, Pamela Soltis said.

And because many plants have medicinal benefits, the genetic data offered by the 1KP project could lead to new discoveries that improve human health.

We focused on getting a lot of wild samples collected from plant lineages known to have important chemistry in hopes that people could mine this material for new compounds, Douglas Soltis said.

The sequences generated by the 1KP team are publicly accessible through the CyVerse Data Commons.

Probably hundreds of papers have used the data in ways we dont even know about, Pamela Soltis said. That is a super cool aspect of this study.

But the 1KP team has little time to celebrate its achievement. The next goal? Sequencing 10,000 genomes.

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Reference: One thousand plant transcriptomes and the phylogenomics of green plants by One Thousand Plant Transcriptomes Initiative, 23 October 2019, Nature.DOI: 10.1038/s41586-019-1693-2

Matthew Gitzendanner, Evgeny Mavrodiev and Grant Godden of the Florida Museum and Emily Sessa of UFs department of biologyalso co-authored the study. James Leebens-Mack of the University of Georgia is a co-corresponding author.

The 1KP initiative was funded by the Alberta Ministry of Advanced Education and Alberta Innovates, Musea Ventures, the National Key Research and Development Program of China, the Ministry of Science and Technology of the Peoples Republic of China, the State Key Laboratory of Agricultural Genomics and the Guangdong Provincial Key Laboratory. Sequencing activities at BGI were also supported by the Shenzhen Municipal Government of China. Computational support was provided by the China National GeneBank, the Texas Advanced Computing Center, WestGrid and Compute Canada. Additional support was provided by the National Science Foundation, the NSF-funded iPlant Collaborative, the National Institutes of Health, German Research Foundation and the Natural Sciences and Engineering Research Council of Canada.

The quote above from Gane Ka-Shu Wong first appeared in a joint press release published by the University of Georgia and the University of Alberta.

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1 Billion Years of Evolution Illuminated by Genetic Sequencing of 1,100 Plants - SciTechDaily

Gene sequences for more than 1100 plant species have been released by an international consortium of nearly 200 plant scientists who were involved in a nine-year research project,One Thousand Plant Transcriptomes Initiative(1KP), that examined the diversification of plant species, genes and genomes across the more than one-billion-year history of green plants dating back to the ancestors of flowering plants and green algae.

Their findings, One Thousand Plant Transcriptomes and Phylogenomics of Green Plants, published today inNature, (doi: 10.1038/s41586-019-1693-2) reveal the timing of whole genome duplications and the origins, expansions and contractions of gene families contributing to fundamental genetic innovations enabling the evolution of green algae, mosses, ferns, conifer trees, flowering plants and all other green plant lineages. The history of how and when plants secured the ability to grow tall, and make seeds, flowers and fruits provides a framework for understanding plant diversity around the planet including annual crops and forest tree species. Sequences, sequence alignments and tree data are available through theCyVerse Data Commons.

Plants have evolved to produce numerous useful chemicals. This study provides insight into that evolutionary process, saidToni Kutchan, Ph.D., vice president for Research and Oliver M. Langenberg Distinguished Investigator,Donald Danforth Plant Science Center. Megan Augustin, research associate, Alex Harkess, Ph.D., postdoctoral associate and Michael McKain, Ph.D., former postdoctoral associate at the Danforth Center also contributed to the research.

Over 100 taxonomic specialists contributed material from field and living collections that include theCentral Collection of Algal Cultures,Royal Botanic Gardens, Kew,Royal Botanic Garden Edinburgh,Atlanta Botanical Garden,New York Botanical Garden,Fairylake Botanical Garden, Shenzhen,TheFlorida Museum of Natural History,Duke University,University of British Columbia Botanical GardenandThe University of Alberta. By sequencing and analyzing genes from a broad sampling of plant species, researchers are better able to reconstruct gene content in the ancestors of all crops and model plant species, and gain a more complete picture of the gene and genome duplications that enabled evolutionary innovations.

In the tree of life, everything is interrelated, saidGane Ka-Shu Wong, lead investigator and professor in the University of AlbertasDepartment of Biological Sciences. Our inferred relationships among living plant species inform us that over the billion years since an ancestral green algal species split into two separate evolutionary lineages, one including flowering plants, land plants and related algal groups and the other comprising a diverse array of green algae, plant evolution has been punctuated with innovations and periods of rapid diversification saidJames Leebens-Mack, professor of plant biology in theUniversity of Georgia Franklin College of Arts and Sciencesand co-corresponding author on the study.

The massive scope of the project demanded development and refinement of new computational tools for sequence assembly and phylogenetic analysis. New algorithms were developed for inferring evolutionary relationships from hundreds of gene sequences for over one thousand species, addressing substantial heterogeneity in evolutionary histories across the genomes.

The Somekh Family Foundation, Government of Alberta and a sequencing commitment from BGI in Shenzhen, China, were secured by Wong to launch 1KP. Once the project was operational, additional resources came from other ongoing projects, including iPlant (nowCyVerse) funded by theU.S. National Science Foundation.

Reference:One Thousand Plant Transcriptomes Initiative. 2019.One thousand plant transcriptomes and the phylogenomics of green plants. Nature.DOI: https://doi.org/10.1038/s41586-019-1693-2.

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Gene Sequences Shed Light on Over One Billion Years of Green Plant Evolution - Technology Networks