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Pop-Up Testing Lab Shows Volunteer Spirit Against Deadly Pandemic

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Caption: Postdoc Jenny Hamilton volunteered to work on coronavirus testing at the Innovative Genomics Institute. Behind her is one of the lab’s liquid-handling systems, which robotically extracts RNA from patient samples before another machine can detect whether that RNA comes from the coronavirus. Credit: Max & Jules Photography.

On March 19, 2020, California became the first U. S. state to issue a stay-at-home order to halt the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19. The order shuttered research labs around the state, and thousands of scientists began sheltering at home and shifting their daily focus to writing papers and grants, analyzing data from past experiments, and catching up on their scientific reading.

That wasn’t the case for everyone. Some considered the order as presenting a perfect opportunity to volunteer, sometimes outside of their fields of expertise, to help their state and communities respond to the pandemic.

One of those willing to pitch in is Jennifer Doudna, University of California, Berkeley (UC Berkeley) and executive director of the school’s Innovative Genomics Institute (IGI), a partnership with the University of California, San Francisco (UC San Francisco). She is also recognized as a pioneer in the development of the popular gene-editing technology called CRISPR.

Doudna, an NIH-supported structural biochemist with no experience in virology or clinical diagnostics, decided that she and her IGI colleagues could establish a pop-up testing lab at their facility. Their job: boost the SARS-CoV-2 testing capacity in her community.

It was a great idea, but a difficult one to execute. The first daunting step was acquiring Clinical Laboratory Improvement Amendments (CLIA) certification. This U. S. certification ensures that quality standards are met for laboratory testing of human blood, body fluid, and other specimens for medical purposes. CLIA certification is required not only to perform such testing in the IGI lab space, but for Doudna’s graduate students, postdocs, and volunteers to process patient samples.

Still, fate was on their side. Doudna and her team partnered with UC Berkeley’s University Health Services to extend the student health center’s existing CLIA certification to the IGI space. And because of the urgency of the pandemic, federal review of the extension request was expedited and granted in a few weeks.

The next challenge was technological. Doudna’s team had to make sure that its diagnostic system was as good or better than those of other SARS-CoV-2 testing platforms. With great care and attention to lab safety, the team began assembling two parallel workstreams: one a semi-manual method to get going right away and the other a faster, automated, robotic method to transition to when ready.

Soon, patient samples began arriving in the lab to be tested for the presence of genetic material (RNA) from SARS-CoV-2, an indication that a person is infected with the virus. The diagnostic system was also soon humming along, with Doudna’s automated workstream having the capacity to process 384 samples in parallel.

The pop-up lab—known formally as the IGI SARS-CoV-2 Diagnostic Testing Laboratory—is funded through philanthropy and staffed by more than 50 volunteers from IGI, UC Berkeley, UC San Francisco, and local data-management companies. Starting on April 6, the lab was fully operational, capable of running hundreds of tests daily with a 24-hour turnaround time for results. A positive test requires that at least two out of three SARS-CoV-2 genomic targets return a positive signal, and the method uses de-identified barcoded sample data to protect patient privacy.

Doudna intends to keep the pop-up lab open as long as her community needs it. So far, they’ve provided testing to UC Berkeley students and staff, first responders (including the entire Berkeley Fire Department), and several members of the city’s homeless population. She says that availability of samples will soon be the rate-limiting step in their sample-analysis pipeline and hopes continued partnerships with local health officials will enable them to work at full capacity to deliver thousands of test results rapidly.

Doudna says she’s been amazed by the team spirit of her lab members and other local colleagues who have come together around a crisis. They’ve gotten the job done by contributing their different skills and resources, including behind-the-scenes efforts by the university’s leadership and staff, philanthropists, city officials, and state government workers.

Although Doudna and her team intend to publish their work to help others follow suit [1], she says the experience has also provided her with many intangible rewards. It has highlighted the value of resilience and adaptation, as well as given her a newfound appreciation for the complexity and precision of operations in the commercial clinical labs that are a routine part of our medical care.

Although the COVID-19 pandemic seems to have thrust all of us into a time warp, in which weeks sometimes feel like months, there is much to do. The amount of work needed to tame this virus is significant and requires an all-hands-on-deck mentality, which NIH and the biomedical research community have embraced fully.

Doudna is not alone. Other labs around the country are engaged in similar efforts. At the NIH’s main campus in Bethesda, MD, staff at the clinical laboratory in the Clinical Center rapidly set up testing for SARS-CoV-2 RNA, and have now tested more than 1,000 NIH staff. Researchers at the Broad Institute of MIT and Harvard partnered with the city of Cambridge, MA, to pilot COVID-19 surveillance in homeless shelters and skilled nursing and assisted living facilities located there.

Hats off to everyone who goes the extra mile to get us through this tough time. I am so gratified when, guided by compassion and dogged determination of the human spirit, science leads the way and provides much needed hope for our future.

Reference:

[1] Blueprint for a Pop-up SARS-CoV-2 Testing Lab. Innovative Genomics Institute SARS-CoV-2 Testing Consortium, Hockemeyer D, Fyodor U, Doudna JA. 2020. medRxiv. Preprint posted on April 12, 2020.

Links:

Coronavirus (COVID-19) (NIH)

CLIA Law & Regulations (Centers for Disease Control and Prevention)

Innovative Genomic Institute (Berkeley, CA)

Doudna Lab (University of California, Berkeley)


Study Finds Nearly Everyone Who Recovers From COVID-19 Makes Coronavirus Antibodies

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Credit: NIH

There’s been a lot of excitement about the potential of antibody-based blood tests, also known as serology tests, to help contain the coronavirus disease 2019 (COVID-19) pandemic. There’s also an awareness that more research is needed to determine when—or even if—people infected with SARS-CoV-2, the novel coronavirus that causes COVID-19, produce antibodies that may protect them from re-infection.

A recent study in Nature Medicine brings much-needed clarity, along with renewed enthusiasm, to efforts to develop and implement widescale antibody testing for SARS-CoV-2 [1]. Antibodies are blood proteins produced by the immune system to fight foreign invaders like viruses, and may help to ward off future attacks by those same invaders.

In their study of blood drawn from 285 people hospitalized with severe COVID-19, researchers in China, led by Ai-Long Huang, Chongqing Medical University, found that all had developed SARS-CoV-2 specific antibodies within two to three weeks of their first symptoms. Although more follow-up work is needed to determine just how protective these antibodies are and for how long, these findings suggest that the immune systems of people who survive COVID-19 have been be primed to recognize SARS-CoV-2 and possibly thwart a second infection.

Specifically, the researchers determined that nearly all of the 285 patients studied produced a type of antibody called IgM, which is the first antibody that the body makes when fighting an infection. Though only about 40 percent produced IgM in the first week after onset of COVID-19, that number increased steadily to almost 95 percent two weeks later. All of these patients also produced a type of antibody called IgG. While IgG often appears a little later after acute infection, it has the potential to confer sustained immunity.

To confirm their results, the researchers turned to another group of 69 people diagnosed with COVID-19. The researchers collected blood samples from each person upon admission to the hospital and every three days thereafter until discharge. The team found that, with the exception of one woman and her daughter, the patients produced specific antibodies against SARS-CoV-2 within 20 days of their first symptoms of COVID-19.

Meanwhile, innovative efforts are being made on the federal level to advance COVID-19 testing. The NIH just launched the Rapid Acceleration of Diagnostics (RADx) Initiative to support a variety of research activities aimed at improving detection of the virus. As I recently highlighted on this blog, one key component of RADx is a “shark tank”-like competition to encourage science and engineering’s most inventive minds to develop rapid, easy-to-use technologies to test for the presence of SARS-CoV-2.

On the serology testing side, the NIH’s National Cancer Institute has been checking out kits that are designed to detect antibodies to SARS-CoV-2 and have found mixed results. In response, the Food and Drug Administration just issued its updated policy on antibody tests for COVID-19. This guidance sets forth precise standards for laboratories and commercial manufacturers that will help to speed the availability of high-quality antibody tests, which in turn will expand the capacity for rapid and widespread testing in the United States.

Finally, it’s important to keep in mind that there are two different types of SARS-CoV-2 tests. Those that test for the presence of viral nucleic acid or protein are used to identify people who are acutely infected and should be immediately quarantined. Tests for IgM and/or IgG antibodies to the virus, if well-validated, indicate a person has previously been infected with COVID-19 and is now potentially immune. Two very different types of tests—two very different meanings.

There’s still a way to go with both virus and antibody testing for COVID-19. But as this study and others begin to piece together the complex puzzle of antibody-mediated immunity, it will be possible to learn more about the human body’s response to SARS-CoV-2 and home in on our goal of achieving safe, effective, and sustained protection against this devastating disease.

Reference:

[1] Antibody responses to SARS-CoV-2 in patients with COVID-19. Long QX, Huang AI, et al. Nat Med. 2020 Apr 29. [Epub ahead of print]

Links:

Coronaviruses (NIH)

NIH Begins Study to Quantify Undetected Cases of Coronavirus Infection,” NIH News Release, April 10, 2020.

NIH mobilizes national innovation initiative for COVID-19 diagnostics,” NIH News Release, April 29, 2020.

Policy for Coronavirus Disease-2019 Tests During the Public Health Emergency (Revised), May 2020 (Food and Drug Administration)


Rising to the COVID-19 Challenge: Rapid Acceleration of Diagnostics (RADx)

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NIH Rapid Acceleration of Diagnostics (RADx) Initiative for COVID-19
Credit: NIH

Step into any major medical center, and you will see the amazing power of technology at work. From X-rays to functional MRIs, blood typing to DNA sequencing, heart-lung machines to robotic surgery, the progress that biomedical technology has made over the past century or so stands as a testament to human ingenuity—and its ability to rise to the all-important challenge of saving lives and improving health.

Today, our nation is in the midst of trying to contain a most formidable health threat: the global coronavirus disease 2019 (COVID-19) pandemic. I’m convinced that biomedical technology has a vital role to play in this urgent effort, which is why the NIH today launched the Rapid Acceleration of Diagnostics (RADx) Initiative.

Fueled by a bold $1.5 billion investment made possible by federal stimulus funding, RADx is an urgent call for science and engineering’s most inventive and visionary minds—from the basement to the board room—to develop rapid, easy-to-use testing technologies for SARS-CoV-2, the novel coronavirus that causes COVID-19. To achieve this, NIH will work closely with our colleagues at the Biomedical Advanced Research and Development Authority, the Centers for Disease Control and Prevention, and the Food and Drug Administration.

If all goes well, RADx aims to support innovative technologies that will make millions more rapid SARS-CoV-2 tests available to Americans by late summer or fall. Such widespread testing, which will facilitate the speedy identification and quarantine of infected individuals and their contacts, will likely be a critical component of making it possible for Americans to get safely back into public spaces, including returning to work and school.

For history buffs and tech geeks, the RADx acronym might ring a bell. During the World War II era, it was the brainstorming of MIT’s “Rad Lab” that gave birth to radar—a groundbreaking technology that, for the first time, enabled humans to use radio waves to “see” planes, storm systems, and many other things. Radar played such a valuable role in finding bombing targets, directing gunfire, and locating enemy aircraft, ships, and artillery that some have argued that this technology actually won the war for the U.S. and its Allies.

As for NIH’s RADx, our aim is to speed the development and commercialization of tests that can rapidly “see” if people have been infected with SARS-CoV-2 with very high sensitivity and specificity, meaning there would be few false negatives and false positives. A key part of this effort, which started today, will be a national technology development competition that’s open to all comers. In this competition, which begins a bit like a “shark tank,” participants will vie for an ultimate share of an approximately $500 million fund that will be awarded to help advance the most-promising testing technologies.

The proposals will undergo an initial review for technical, clinical, commercial, and regulatory issues. For example, could the testing technology be easily scaled up? Would it provide clear advantages over existing approaches? And would the U.S. health-care system realistically be able to adopt the technology rapidly? If selected, the proposals will then enter a three-phase process that will run into summer. Each development team will receive its own initial budget, deadlines, and set of deliverables. Competitors must also work collaboratively with an assigned expert and utilize associated web-based tools.

As you see in the graphic above, each phase will whittle down the competition. Those testing technologies that succeed in making it to Phase 2 will receive an appropriate budget to enable full clinical deployment on an accelerated timeline. They will also be matched with technical, business, and manufacturing experts to boost their chances of success.

Of course, not all technologies will enter the competition at the same stages of development. Those that are already relatively far along will be “fast tracked” to a phase that corresponds with their place in the commercialization process. Our hope is that the winning technologies will feature patient- and user-friendly designs, mobile-device integration, affordable cost, and increased accessibility, for use at the point of care (or even at home).

To assist competitors in their efforts to accomplish these bold goals, RADx will expand the Point-of-Care Technologies Research Network, which was established several years ago by NIH’s National Institute of Biomedical Imaging and Bioengineering (NIBIB). The network supports hundreds of investigators through five technology hubs at: Emory University/Georgia Institute of Technology, Atlanta; Johns Hopkins University, Baltimore; Northwestern University, Evanston, IL; University of Massachusetts Medical School, Worcester; and the Consortia for Improving Medicine with Innovation & Technology at Harvard Medical School/Massachusetts General Hospital, Boston.

RADx is focused on diagnostic testing, but NIH is also intensely engaged in developing safe, effective therapies and vaccines for COVID-19. One innovative effort, called Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV), is a public-private partnership that aims to speed the development of ways to treat and prevent this disease that’s caused so much suffering and death around the globe.

So, to the U.S. science and engineering community, I have these words: Let’s get going—our nation has never needed your skills more!

Links:

Coronavirus (COVID-19) (NIH)

NIH mobilizes national innovation initiative for COVID-19 diagnostics, NIH news release, April 29, 2020

Point-of-Care Technologies Research Network (National Institute of Biomedical Imaging and Biotechnology/NIH)

NIH to launch public-private partnership to speed COVID-19 vaccine and treatment options, NIH news release, April 17, 2020.

We Need More COVID-19 Tests. We Propose a ‘Shark Tank’ to Get There, Lamar Alexander, Roy Blunt. Washington Post, April 20, 2020.


Getting Closer to a Blood Test for Alzheimer’s Disease?

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Blood Test
iStock/ericsphotography

As research on Alzheimer’s disease (AD) advances, a desperate need remains for an easy blood test to help diagnose the condition as early as possible. Ideally, such a test could also distinguish AD from other forms of dementia that produce similar symptoms. As published recently in Nature Medicine, an NIH-funded research team has designed a simple blood test that is on course to meet these criteria [1].

The latest work builds on a large body of work showing that one secret to predicting a person’s cognitive decline and treatment response in AD lies in a protein called tau. Using the powerful, but expensive, approach of PET scan imaging, we know that tau builds up in the brain as Alzheimer’s disease progresses. We also know that some tau spills from the brain into the bloodstream.

The trouble is that the circulating tau protein breaks down far too quickly for a blood test to offer a reliable measure of what’s happening in a person’s brain. A few years ago, researchers discovered a possible solution: test for blood levels of a slightly different and more stable version of the protein called pTau181 [2]. (The “p” in its name comes from the addition of phosphorus in a particular part of the protein’s structure.)

In the latest study, researchers in the lab of Adam Boxer, University of California, San Francisco, followed up further on this compelling lead. Boxer’s team measured pTau181 levels in blood samples from 362 people between the ages of 58 and 70. Those samples included 56 people with an Alzheimer’s diagnosis, along with 47 people with mild cognitive impairment and 69 healthy controls.

The researchers also included another 190 people diagnosed with frontotemporal lobar degeneration (FTLD). It is a relatively rare form of dementia that leads to a gradual decline in behavior, language, and movement, often in connection with a buildup of tau in the brain.

The study found that levels of pTau181 were roughly 3.5-times higher in the blood of people with AD compared to people without AD. Those with mild cognitive impairment due to underlying AD also showed an intermediate increase in blood levels of pTau181.

Importantly, people with FLTD had normal blood levels of pTau181. As a result, the blood test could reliably distinguish between a person with AD and a person with FLTD. That’s important because, while FLTD is a relatively rare condition, its prevalence is similar to AD in people under the age of 65. But both conditions have similar symptoms, making it often challenging to distinguish them.

The findings add to evidence that the new blood test can help in diagnosing AD and in distinguishing it from other neurodegenerative conditions. In fact, it does so with an accuracy that often rivals more expensive PET scans and more invasive cerebrospinal fluid tests, which are now the only reliable ways to measure tau.

There’s still plenty of work to do before this blood test is ready for a doctor’s office. But these initial findings are very promising in helping to simplify the diagnosis of this devastating condition that now affects an estimated 5.5 million Americans [3].

References:

[1] Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Thijssen EH, La Joie R, Wolf A, Strom A, Wang P, Iaccarino L, Bourakova V, Cobigo Y, Heuer H, Spina S, VandeVrede L, Chai X, Proctor NK, Airey DC, Shcherbinin S, Duggan Evans C, Sims JR, Zetterberg H, Blennow K, Karydas AM, Teunissen CE, Kramer JH, Grinberg LT, Seeley WW, Rosen H, Boeve BF, Miller BL, Rabinovici GD, Dage JL, Rojas JC, Boxer AL; Advancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) investigators. Nat Med. 2020 Mar 2.

[2] Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Mielke MM, Hagen CE, Xu J, Chai X, Vemuri P, Lowe VJ, Airey DC, Knopman DS, Roberts RO, Machulda MM, Jack CR Jr, Petersen RC, Dage JL. Alzheimers Dement. 2018 Aug;14(8):989-997.

[3] Alzheimer’s Disease Fact Sheet. National Institute on Aging, May 22, 2019.

Links:

Alzheimer’s Disease & Related Dementias (National Institute on Aging/NIH)

What Are Frontotemporal Disorders? (NIA)

Accelerating Medicines Partnership: Alzheimer’s Disease (NIH)

Adam Boxer (University of California, San Francisco)

NIH Support: National Institute on Aging; National Institute of Neurological Disorders and Stroke; National Center for Advancing Translational Sciences


Whole-Genome Sequencing Plus AI Yields Same-Day Genetic Diagnoses

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Sebastiana
Caption: Rapid whole-genome sequencing helped doctors diagnose Sebastiana Manuel with Ohtahara syndrome, a neurological condition that causes seizures. Her data are now being used as part of an effort to speed the diagnosis of other children born with unexplained illnesses. Credits: Getty Images (left); Jenny Siegwart (right).



Back in April 2003, when the international Human Genome Project successfully completed the first reference sequence of the human DNA blueprint, we were thrilled to have achieved that feat in just 13 years. Sure, the U.S. contribution to that first human reference sequence cost an estimated $400 million, but we knew (or at least we hoped) that the costs would come down quickly, and the speed would accelerate. How far we’ve come since then! A new study shows that whole genome sequencing—combined with artificial intelligence (AI)—can now be used to diagnose genetic diseases in seriously ill babies in less than 24 hours.

Take a moment to absorb this. I would submit that there is no other technology in the history of planet Earth that has experienced this degree of progress in speed and affordability. And, at the same time, DNA sequence technology has achieved spectacularly high levels of accuracy. The time-honored adage that you can only get two out of three for “faster, better, and cheaper” has been broken—all three have been dramatically enhanced by the advances of the last 16 years.

Rapid diagnosis is critical for infants born with mysterious conditions because it enables them to receive potentially life-saving interventions as soon as possible after birth. In a study in Science Translational Medicine, NIH-funded researchers describe development of a highly automated, genome-sequencing pipeline that’s capable of routinely delivering a diagnosis to anxious parents and health-care professionals dramatically earlier than typically has been possible [1].

While the cost of rapid DNA sequencing continues to fall, challenges remain in utilizing this valuable tool to make quick diagnostic decisions. In most clinical settings, the wait for whole-genome sequencing results still runs more than two weeks. Attempts to obtain faster results also have been labor intensive, requiring dedicated teams of experts to sift through the data, one sample at a time.

In the new study, a research team led by Stephen Kingsmore, Rady Children’s Institute for Genomic Medicine, San Diego, CA, describes a streamlined approach that accelerates every step in the process, making it possible to obtain whole-genome test results in a median time of about 20 hours and with much less manual labor. They propose that the system could deliver answers for 30 patients per week using a single genome sequencing instrument.

Here’s how it works: Instead of manually preparing blood samples, his team used special microbeads to isolate DNA much more rapidly with very little labor. The approach reduced the time for sample preparation from 10 hours to less than three. Then, using a state-of-the-art DNA sequencer, they sequence those samples to obtain good quality whole genome data in just 15.5 hours.

The next potentially time-consuming challenge is making sense of all that data. To speed up the analysis, Kingsmore’s team took advantage of a machine-learning system called MOON. The automated platform sifts through all the data using artificial intelligence to search for potentially disease-causing variants.

The researchers paired MOON with a clinical language processing system, which allowed them to extract relevant information from the child’s electronic health records within seconds. Teaming that patient-specific information with data on more than 13,000 known genetic diseases in the scientific literature, the machine-learning system could pick out a likely disease-causing mutation out of 4.5 million potential variants in an impressive 5 minutes or less!

To put the system to the test, the researchers first evaluated its ability to reach a correct diagnosis in a sample of 101 children with 105 previously diagnosed genetic diseases. In nearly every case, the automated diagnosis matched the opinions reached previously via the more lengthy and laborious manual interpretation of experts.

Next, the researchers tested the automated system in assisting diagnosis of seven seriously ill infants in the intensive care unit, and three previously diagnosed infants. They showed that their automated system could reach a diagnosis in less than 20 hours. That’s compared to the fastest manual approach, which typically took about 48 hours. The automated system also required about 90 percent less manpower.

The system nailed a rapid diagnosis for 3 of 7 infants without returning any false-positive results. Those diagnoses were made with an average time savings of more than 22 hours. In each case, the early diagnosis immediately influenced the treatment those children received. That’s key given that, for young children suffering from serious and unexplained symptoms such as seizures, metabolic abnormalities, or immunodeficiencies, time is of the essence.

Of course, artificial intelligence may never replace doctors and other healthcare providers. Kingsmore notes that 106 years after the invention of the autopilot, two pilots are still required to fly a commercial aircraft. Likewise, health care decisions based on genome interpretation also will continue to require the expertise of skilled physicians.

Still, such a rapid automated system will prove incredibly useful. For instance, this system can provide immediate provisional diagnosis, allowing the experts to focus their attention on more difficult unsolved cases or other needs. It may also prove useful in re-evaluating the evidence in the many cases in which manual interpretation by experts fails to provide an answer.

The automated system may also be useful for periodically reanalyzing data in the many cases that remain unsolved. Keeping up with such reanalysis is a particular challenge considering that researchers continue to discover hundreds of disease-associated genes and thousands of variants each and every year. The hope is that in the years ahead, the combination of whole genome sequencing, artificial intelligence, and expert care will make all the difference in the lives of many more seriously ill babies and their families.

Reference:

[1] Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, Ellsworth K, Camp B, Kint CI, Yacoubian C, Farnaes L, Bainbridge MN, Beebe C, Braun JJA, Bray M, Carroll J, Cakici JA, Caylor SA, Clarke C, Creed MP, Friedman J, Frith A, Gain R, Gaughran M, George S, Gilmer S, Gleeson J, Gore J, Grunenwald H, Hovey RL, Janes ML, Lin K, McDonagh PD, McBride K, Mulrooney P, Nahas S, Oh D, Oriol A, Puckett L, Rady Z, Reese MG, Ryu J, Salz L, Sanford E, Stewart L, Sweeney N, Tokita M, Van Der Kraan L, White S, Wigby K, Williams B, Wong T, Wright MS, Yamada C, Schols P, Reynders J, Hall K, Dimmock D, Veeraraghavan N, Defay T, Kingsmore SF. Sci Transl Med. 2019 Apr 24;11(489).

Links:

DNA Sequencing Fact Sheet (National Human Genome Research Institute/NIH)

Genomics and Medicine (NHGRI/NIH)

Genetic and Rare Disease Information Center (National Center for Advancing Translational Sciences/NIH)

Stephen Kingsmore (Rady Children’s Institute for Genomic Medicine, San Diego, CA)

NIH Support: National Institute of Child Health and Human Development; National Human Genome Research Institute; National Center for Advancing Translational Sciences


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