Monday, July 1, 2019
Summer has finally arrived, and it's once again time to shine some light on NIH's rich history. Over the past couple months, NIH has celebrated several important anniversaries, including the 20th birthday of NIH's Vaccine Research Center and the 70th anniversary of the NIH Record newsletter. Read on to learn more about these milestones and other fun facts and intriguing objects from NIH's past!
Supercomputing Helps IRP Researchers Complete Our Genetic Blueprints
Monday, April 22, 2019
While the Human Genome Project accomplished a remarkable feat in sequencing all the genes in the human genome, technological limitations still left significant swaths of our genetic blueprints unexplored. Recent advances in DNA sequencing are starting to fill in those gaps, but these new technologies require new computational tools to make sense of the data they generate. That’s where computer scientists like the IRP’s Adam Phillippy, Ph.D., come in.
Thursday, March 14, 2019
Surrounded as we are with incredible technologies like supercomputers, MRI scanners, and smartphones, it's easy to forget that technologies viewed as antiquated today were once considered cutting-edge. Perhaps learning about some of the gadgets and technological concerns from NIH's past will help spark a greater appreciation for the wonderful gizmos that are spurring new scientific discoveries (and adorable cat memes) today.
Tuesday, January 29, 2019
Virtually all parents would agree that having kids is a massive undertaking, and not just after they’re born. Many couples struggle to conceive, and each year thousands of American women experience complications when giving birth. With the help of the NIH’s state-of-the-art supercomputer, Biowulf, IRP senior investigator Rajeshwari Sundaram, Ph.D., develops and refines statistical tools that can guide prospective parents and their doctors through these challenges.
Wednesday, January 2, 2019
The waning weeks of 2018 were busy ones in the Office of NIH History. We're constantly receiving and cataloguing new donations of historic equipment, images, publications, and more. It’s time to see what our donors have given us lately!
"I thought why could you not invert the concept? Instead of laying down hundreds or thousands of probes, how about laying down hundreds or thousands of tissue spots and probing them one antibody or gene probe at a time," remembers Dr. Juha Kononen of the National Human Genome Research Institute (NHGRI) about his idea that led to this prototype manual microarray. Tissue array technology performs rapid molecular profiling of hundreds of normal and pathological tissue specimens or cultured cells. Dr. Kononen worked with Drs. Olli Kallioniemi and Stephen B. Leighton to design this tissue microarray which was initially used in the Cancer Genomics Branch. Now, the National Cancer Institute's Tissue Array Research Program (TARP) develops and distributes multi-tumor tissue microarray slides to cancer researchers based on this technology. The quote comes from a 2002 article published in The Scientist.
Monday, December 17, 2018
Science is a process of trial and error. Most successful research publications are preceded by at least a few false starts and perhaps weeks or even months of tinkering to get experiments to work. For IRP senior investigator Carson Chow, Ph.D., this process of testing and throwing out one potential solution after another is an essential part of his research, so much so that he may go through thousands of iterations before arriving at one that works. However, rather than test each approach himself, he leverages the IRP’s considerable computing power to considerably accelerate the process of sorting the wheat from the chaff.
Tuesday, November 27, 2018
The human genome comprises roughly three billion base pairs and around 20,000 protein-coding genes, according to recent estimates. That’s a lot of information crammed into the tiny nucleus of a cell, and it doesn’t even include the many genes that do not produce a protein or the fact that most genes come in multiple flavors that vary in different individuals. Add to that the phenomenon of an identical gene being either more or less active in two different people and you can quickly end up with genomic datasets that would overload nearly any computer. Fortunately for IRP senior investigator Daniel Levy, M.D., the NIH IRP has one of the few computer systems in the world that can handle this mountain of information.
Thursday, August 2, 2018
For most of their history, computers have been limited to mindlessly executing the instructions their programmers give them. However, recent advances have given rise to the intertwined fields of artificial intelligence (AI) and machine learning, which focus on the creation of computer programs that can operate independently and even teach themselves to perform specific, specialized tasks. In 2013, the online PubMed database listed only 200 research publications related to ‘deep learning,’ a new type of machine learning that has shown success for particularly difficult tasks like object and speech recognition. Just four years later, in 2017, that number exceeded 1,100.
Wednesday, June 13, 2018
Access to robust computing resources provides a critical foundation for advancing the wide variety of biomedical research taking place within the NIH’s Intramural Research Program (IRP). Whether performing molecular modeling simulations, generating whole-genome sequencing data, deducing the structures of biomolecules, or advancing drug discovery efforts, our ability to analyze large-scale biological and biomedical data strongly depends on our ability to employ computationally intensive approaches that produce interpretable results and advance translational efforts aimed at improving human health.
Thursday, March 29, 2018
Alex Fuksenko, a senior at the University of Maryland in College Park, spent his summer in the lab of NIH IRP Investigator Kevin Briggman, Ph.D.
Fuksenko helped to create a website called Labrainth that “gamifies” the identification and tracing of neurons in 2D images produced by electron microscopes. By visiting the website and completing those activities, members of the public can earn points and move up leaderboards while producing data that machine learning algorithms can use to learn how to trace neurons in these images themselves, a necessary step towards producing an accurate 3D model of the human brain.