Tag: Bioinformatics

Cloudy With a Chance of Scientific Discoveries

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The cloud. To many, it’s a mysterious black hole that somehow transports photos and files from their old or lost phone to their new one. To some researchers, though, it’s an invaluable resource that allows them access to data analytics tools they wouldn’t otherwise have.

Hands typing on a laptop with a digitized cloud and computer icons floating above them.
Credit: iStock.

Scientists have begun using cloud computing to store, process, and analyze their data through online bioinformatics tools. Biological data sets are often large and hard to interpret, requiring complex calculating instructions—or algorithms—to understand them. Fortunately, these algorithms can run on local computers or remotely through cloud computing.

One advantage of cloud-based programs over local computers is the ability to analyze data without taking up the user’s personal storage space. With cloud-based storage, researchers can store their large data files, including their labeled notes called annotations. Another benefit is that users have easy access to software packages within the cloud for data analysis. The cloud also encourages collaboration among scientists by making it easy to share large amounts of data.

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Career Conversations: Q&A with Biological Engineer Brian Munsky

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A headshot of Dr. Brian Munsky. Dr. Brian Munsky. Credit: Colorado State University.

“I think having a career in science is really the best way to rechannel the inner child, to remain forever curious about the world,” says Brian Munsky, Ph.D., an associate professor of chemical and biological engineering at Colorado State University, Fort Collins. Check out the highlights of our interview with Dr. Munsky below to learn how his childhood practical jokes led to him running a research group that uses computational and experimental methods to study complex processes inside cells.

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Biology Beyond the Lab: Using Computers to Study Life

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A headshot of Dr. Melissa Wilson.
Learn more about Dr. Melissa Wilson’s computational biology research in another Biomedical Beat blog post. Credit: Jacob Sahertian, ASU.

“You’re not going to be able to do biology without understanding programming in the future,” Melissa Wilson, Ph.D., an associate professor of genomics, evolution, and bioinformatics at Arizona State University, said in her 2019 NIGMS Early Career Investigator Lecture. “You don’t have to be an expert programmer. But without understanding programming, I can assert you won’t be able to do biology in the next 20 years.”

A growing number of researchers, like Dr. Wilson, are studying biology using computers and mathematical methods. Some of them started in traditional biology or other life science labs, while others studied computer science or math first. Here, we’re featuring two researchers who took different paths to computational biology.

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Gone Fishing: Teaching Bioinformatics With Skate DNA

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As computers have advanced over the past few decades, researchers have been able to work with larger and more complex datasets than ever before. The science of using computers to investigate biological data is called bioinformatics, and it’s helping scientists make important discoveries, such as finding versions of genes that affect a person’s risk for developing various types of cancer. Many scientists believe that almost all biologists will use bioinformatics to some degree in the future.

A cluster of various-sized dots connected by glowing lines.
Bioinformatics software was used to create this representation of a biological network. Credit: Benjamin King, University of Maine.

However, bioinformatics isn’t always included in college biology programs, and many of today’s researchers received their training before bioinformatics was widely taught. To address these gaps, the bioinformatics cores of the five Northeast IDeA Networks of Biomedical Research Excellence (INBREs)—located in Maine, Rhode Island, Delaware, Vermont, and New Hampshire—have worked together to offer basic bioinformatics training to students and researchers. The collaboration started in 2009 with a project where researchers sequenced the genome of a fish called the little skate (Leucoraja erinacea) and used the data to develop trainings.

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Online Virus Tracking Tool Nextstrain Wins Inaugural Open Science Prize

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Nextstrain’s analysis of the genomes from Zika virus obtained in 25 countries over the past few years. 

Credit: Trevor Bedford and Richard Neher, nextstrain.org.

Over the past decade, scientists and clinicians have eagerly deposited their burgeoning biomedical data into publicly accessible databases. However, a lack of computational tools for sharing and synthesizing the data has prevented this wealth of information from being fully utilized.

In an attempt to unleash the power of open-access data, the National Institutes of Health, in collaboration with the Howard Hughes Medical Institute and Britain’s Wellcome Trust, launched the Open Science Prize Exit icon. Last week, after a multi-stage public voting process, the inaugural award was announced. The winner of the grand prize—and $230,000—is a prototype computational tool called nextstrain Exit icon that tracks the spread of emerging viruses such as Ebola and Zika. This tool could be especially valuable in revealing the transmission patterns and geographic spread of new outbreaks before vaccines are available, such as during the 2013-2016 Ebola epidemic and the current Zika epidemic.

An international team of scientists—led by NIGMS grantee Trevor Bedford of the Fred Hutchinson Cancer Research Center, Seattle, and Richard Neher Exit icon of Biozentrum at the University of Basel, Switzerland—developed nextstrain as an open-access system capable of sharing and analyzing viral genomes. The system mines viral genome sequence data that researchers have made publicly available online. nextstrain then rapidly determines the evolutionary relationships among all the viruses in its database and displays the results of its analyses on an interactive public website.

The image here shows nextstrain’s analysis of the genomes from Zika virus obtained in 25 countries over the past few years. Plotting the relatedness of these viral strains on a timeline provides investigators a sense of how the virus has spread and evolved, and which strains are genetically similar. Researchers can upload genome sequences of newly discovered viral strains—in this case Zika—and find out in short order how their new strain relates to previously discovered strains, which could potentially impact treatment decisions.

Nearly 100 interdisciplinary teams comprising 450 innovators from 45 nations competed for the Open Science Prize. More than 3,500 people from six continents voted online for the winner. Other finalists for the prize focused on brain maps, gene discovery, air-quality monitoring, neuroimaging and drug discovery.

nextstrain was funded in part by NIH under grant U54GM111274.

Student Researcher Finds New Clues About Flu with Old Data

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Do you like to find new uses for old things? Like weaving old shirts into a rug, repurposing bottles into candle holders or turning packing crates into tables? Katie Gostic, a University of California, Los Angeles (UCLA) graduate student, likes finding new uses for old data. She channeled this interest when she analyzed existing data to study whether childhood exposure to flu affects a person’s future immunity to the disease.

Katie Gostic
Gostic conducted research for the flu project during the summer of 2015 when she was visiting her boyfriend, a tropical biologist, in Alamos, Sonora, Mexico. Credit: Charlie de la Rosa.

As an undergraduate student at Princeton University, Gostic was originally pursuing a degree in engineering. Her focus shifted to biology after taking an infectious disease modeling class. Gostic’s background in math and programming allows her to take large, complex pre-existing data sets and reanalyze them using new tools and methods. The result: Information that wasn’t accessible when the data were first collected.

Now a graduate researcher in the ecology and evolutionary biology lab of James Lloyd-Smith Exit icon, Gostic studies infectious diseases. The lab builds mathematical models to investigate zoonotic diseases—diseases that animals can transmit to humans but that humans don’t frequently spread between each other. Examples include diseases caused by Leptospira, a type of bacteria that infects household pets and many other animals, and monkeypox, a virus whose transmission to humans is increasing since the eradication of smallpox. The lab also studies bird flus, a category of flu viruses that infect birds and other animals and only occasionally jump to people. A very small number of cases of human-to-human transmission of bird flus have been recorded. However, if a bird flu virus mutated in a way that allowed it to spread among humans, it could cause a pandemic. Continue reading “Student Researcher Finds New Clues About Flu with Old Data”

El Niño Season Temperatures Linked to Dengue Epidemics

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Screen shot from a video showing dengue incidence in Southeast Asia.
Incidence of dengue fever across Southeast Asia, 1993-2010. Note increasing incidence (red) starting about June 1997, which corresponds to a period of higher temperatures driven by a strong El Niño season. At the end of the El Niño event, in January 1999, dengue incidence is much lower (green). Credit: Wilbert van Panhuis, University of Pittsburgh.

Weather forecasters are already warning about an intense El Niño season that’s expected to alter precipitation levels and temperatures worldwide. El Niño seasons, characterized by warmer Pacific Ocean water along the equator, may impact the spread of some infectious diseases transmitted by mosquitoes.

In a study published last month in the Proceedings of the National Academy of Sciences, researchers reported a link between intense dengue fever epidemics in Southeast Asia and the high temperatures that a previous El Niño weather event brought to that region.

Dengue fever, a viral infection transmitted by the Aedes mosquito, can cause life-threatening high fever, severe joint pain and bleeding. Infection rates soar every two to five years. Interested in understanding why, an international team of researchers collected and analyzed incidence reports including 3.5 million dengue fever cases across eight Southeast Asian countries spanning an 18-year period. The study is part of Project Tycho, an effort to study disease transmission dynamics by mining historical data and making that data freely available to others. Continue reading “El Niño Season Temperatures Linked to Dengue Epidemics”

Data-Mining Study Explores Health Outcomes from Common Heartburn Drugs

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Results of a data-mining study suggest a link between a common heartburn drug and heart attacks. Credit: Stock image.

Scouring through anonymized health records of millions of Americans, data-mining scientists found an association between a common heartburn drug and an elevated risk for heart attacks. Their preliminary results suggest that there may be a link between the two factors.

For 60 million Americans, heartburn is a painful and common occurrence caused by stomach acid rising through the esophagus. It’s treated by drugs such as proton-pump inhibitors (PPIs) that lower acid production in the stomach. Taken by about one in every 14 Americans, PPIs, which include Nexium and Prilosec, are the most popular class of heartburn drugs.

PPIs have long been thought to be completely safe for most users. But a preliminary laboratory study published in 2013 suggested that this may not be the case. The study, led by a team of researchers at Stanford University, showed that PPIs could affect biochemical reactions outside of their regular acid suppression action that would have harmful effects on the heart. Continue reading “Data-Mining Study Explores Health Outcomes from Common Heartburn Drugs”

Digging Deeply Into Data for the Causes of Disease

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Hunting for the cause of a disease can be like tracing a river back to its many sources. Myriad factors, large and small, may contribute to a condition. One approach to the search focuses on the massive amounts of genomic and other biological data that scientists are gathering in the course of their studies. To examine this data and look for meaningful patterns and other clues, scientists turn to bioinformatics, a field focused on the development of analytical methods and software tools.

Here are a few examples of how National Institutes of Health-funded scientists are using bioinformatics to dig deeply into data and learn more about the development of diseases, including Huntington’s, preeclampsia and asthma.

Huntington’s Disease

Network of proteins that interact with huntingtin

Researchers have mapped a network of 2,141 proteins that all interact either directly or through one other protein with huntingtin (red), the protein associated with Huntington’s disease. Credit: Cendrine Tourette, Buck Institute for Research on Aging, J Biol Chem 2014 Mar 7;289(10):6709-26 Exit icon.

The cause of Huntington’s disease, a degenerative neurological disorder with no known cure, may appear simple. It begins with a change in a single gene that alters the shape and functioning of the huntingtin protein. But this protein, whether in its normal or altered form, does not act alone. It interacts with other proteins, which in turn interact with others.

A research team led by Robert Hughes of the Buck Institute for Research on Aging set out to understand how this ripple effect contributes to the breakdown in normal cellular function associated with Huntington’s disease. The scientists used experimental and computational approaches to map a network of 2,141 proteins that interact with the huntingtin protein either directly or through one other protein. They found that many of these proteins were involved in cell movement and intercellular communication. Understanding how the huntingtin protein leads to mistakes in these cellular processes could help scientists pursue new approaches to developing treatments. Continue reading “Digging Deeply Into Data for the Causes of Disease”

Simulating the Potential Spread of Measles

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Try out FRED Measles:

  1. Go to http://fred.publichealth.
    pitt.edu/measles
    Exit icon
  2. Select “Get Started”
  3. Pick a state and city
  4. Play both simulations

To help the public better understand how measles can spread, a team of infectious disease computer modelers at the University of Pittsburgh has launched a free, mobile-friendly tool that lets users simulate measles outbreaks in cities across the country.

The tool is part of the Pitt team’s Framework for Reconstructing Epidemiological Dynamics, or FRED, that it previously developed to simulate flu epidemics. FRED is based on anonymized U.S. census data that captures demographic and geographic distributions of different communities. It also incorporates details about the simulated disease, such as how contagious it is.

Screenshot of the FRED simulation.

A free, mobile-friendly tool lets users simulate potential measles outbreaks in cities across the country. Credit: University of Pittsburgh Graduate School of Public Health.

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