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Jason Fan, a research graduate assistant in the Center for Bioinformatics and Computational Biology (CBCB), recently had a paper on cross-species inference accepted to the journal Nucleic Acids Research.

“Functional Protein Representations from Biological Networks Enable Diverse Cross-Species Inference,” examines a kernel-based method, MUNK, to integrate sequences and network structures to create functional protein representations, embedding proteins from different species in the same vector space.

Fan co-authored the paper with Max Leiserson, an assistant professor of computer science and a member of CBCB, along with collaborators from the University of Washington, University of North Carolina Medical School, Boston University, Princeton University and Northeastern University.

As part of their work, their research showed proteins from different species that are close in MUNK-space and are functionally similar. Next, they used these representations to share knowledge of synthetic lethal interactions between species. Importantly, they found that the results using MUNK-representations were at least as accurate as existing algorithms for these tasks. By generalizing the notion of a phenolog ("orthologous phenotype") to use functionally similar proteins (i.e. those with similar representations), they were able to demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.

To read the full paper, go here.

Mihai Pop, a professor of computer science noted for his scientific research and advocacy for greater equality in academia, has been appointed director of the University of Maryland Institute for Advanced Computer Studies (UMIACS), effective Nov. 25, 2018 through June 30, 2022.

Pop will provide leadership to more than 80 faculty members who hail from 11 departments across the UMD campus, as well as 150 graduate students and almost two dozen administrative and technical staff members who support the institute’s research, innovation and outreach.

He has served as interim director of UMIACS since March 2018, succeeding Amitabh Varshney, who is now dean of the university’s College of Computer, Mathematical, and Natural Sciences.

“Mihai has a strong record of scientific research with impact and has amply demonstrated his ability to move UMIACS forward,” Varshney said. “I look forward to working closely with him as our college and the university expand their scientific scope and educational reach in areas that are closely tied to advances in computing.”

Pop said he is honored to be chosen to lead the institute, noting that the core mission of UMIACS is to pursue interdisciplinary research that will have a positive impact on both science and society.

“I look forward to working with our faculty, postdocs, students and staff to bring the power of modern computing to bear on many of the challenges we face,” Pop said.

UMIACS was launched in 1985, and currently boasts 16 labs and centers focused on areas like computer vision, computational biology, cybersecurity, and natural language processing. Combined with the Department of Computer Science—where 65 percent of UMIACS faculty members have tenure or tenure-track appointments—the institute brings in almost $25 million annually in research funding.

Pop said recent advances in computational power and fresh theoretical approaches have expanded the institute’s research agenda to include substantial activity involving machine learning, the human microbiome, autonomous robotics, quantum information science, and virtual and augmented reality.

He plans to help recruit new faculty members to pursue cutting edge work in these high priority areas, while also capitalizing on other resources including the Brendan Iribe Center slated to open in early 2019, a recently announced collaboration with Capital One, and the institute’s advantageous location near federal labs and agencies in the Washington, D.C. area.

“We have the human talent, the physical infrastructure—and are in the right location, at the right time—to grow our research enterprise significantly,” Pop said. “I would like UMIACS to be better recognized both at the national and international level for the important work we do.”

A strong advocate for bringing more diversity to the field of computer science, Pop said he will continue to support the Maryland Center for Women in Computing and other initiatives that enhance participation in the discipline by people from diverse backgrounds. As interim director, he sponsored activities like Technica, the world’s largest all-women hackathon; the Diversity in Computing Summit; and the Widening Natural Language Processing workshop.

He also intends to provide resources and encouragement to tenure-track and professional-track UMIACS faculty members who are in the early stages of their academic careers.

“The voice of a fresh Ph.D. assistant professor is often louder than that of the most experienced lecturer or research scientist on our campus,” Pop recently wrote in an op-ed piece in The Faculty Voice. “It is high time for a change. Our campus cannot excel if we continue to ignore and undervalue the many faculty who shoulder the bulk of our teaching, and who represent a critical driving force in our research programs. Whether professional or tenure-track faculty, we are all faculty.”

Pop’s own research interests cover several areas of bioinformatics, primarily related to the development of computational algorithms for analyzing biological data generated by high-throughput experimental techniques, such as sequencing technologies.

Part of Pop’s research focuses on the computational analysis of the microbial communities inhabiting our world and our bodies—a scientific field called metagenomics. His lab, part of UMIACS’ Center for Bioinformatics and Computational Biology, has developed a number of software tools that are now widely used in the field. He has also been an active participant in a number of large-scale, multinational projects, including the Human Microbiome Project and the GEMS study of diarrheal disease in children from the developing world.

In 2018, Pop made Clarivate Analytics’ list of Highly Cited Researchers, a compilation of influential names in science whose published work in their specialty areas has consistently been judged by their peers to be of particular use and significance. He was also included in the 2014 list.

In addition to his leadership role in UMIACS, Pop is co-director for the Center for Health-related Informatics and Bioimaging, a partnership between the University of Maryland, College Park and the University of Maryland, Baltimore that is funded by the MPowering the State initiative.

An active educator and mentor, Pop currently advises six graduate students and one postdoc. He previously worked with 11 graduate students and four postdocs, and he spent three years as a faculty adviser to a group of UMD freshmen that analyzed the genome of the diamondback terrapin, the university’s mascot.

Pop earned his B.S. in computer science from University Politehnica of Bucharest in Romania in 1994 and his M.S.E. and Ph.D. in computer science from Johns Hopkins University in 1998 and 2000. He has been at Maryland since 2005.

Sometimes bigger is not necessarily better. Take DNA databases, for example. As a result of substantial advances in computing power, scientists can now rapidly slice and dice genomic data used to identify different species of bacteria, or at least find their close relatives.

This is relevant for researchers wanting to match unknown DNA sequences against a database of known sequences, helping to answer fundamental biological questions such as what organism is this DNA from, or what gene might it encode?

But DNA databases used in these searches have grown so large, so quickly, sometimes undermining the scientific process. This may cause medical or environmental researchers to conclude that their as-yet-unidentified samples contain potentially harmful organisms, when in fact they don’t.

A research scientist in the University of Maryland’s Center for Bioinformatics and Computational Biology (CBCB) has just published an analysis that demonstrates a call for improved methods and algorithms when searching large DNA databases.

Daniel Nasko (in photo), working with experts from Rice University and the National Human Genome Research Institute, detailed current challenges and offered several strategies to improve the accuracy of DNA classifications using high-throughput sequencing tools. The goal is to rapidly classify microbes more confidently.

The research team’s analysis recently appeared in Genome Biology. Nasko was lead author of the study. Todd Treangen (lead researcher on the team from Rice University), Sergey Koren and Adam Phillippy (both from the National Human Genome Research Institute) also contributed.

“This work should highlight that more information does not automatically lead to more knowledge,” says Nasko, who has an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). “By sequencing so many similar bacterial genomes, we’re going to need more advanced algorithms and metrics to determine the biological origin of pieces of DNA.”

For their study, the researchers closely examined RefSeq, a widely used federal database that stores genomes from all of the organisms that have been sequenced. RefSeq currently contains about 1.3 trillion base pairs of data (>1.3 petabytes of data), which makes searching against this database computationally challenging.

The researchers tracked how the addition of more bacterial genomes to the database has made it more difficult for fast algorithms to identify what bacterial species an unknown piece of DNA originated from.

This may be happening because new species of common bacteria are being sequenced over and over again, they say. For example, more than five percent of the bacterial genomes in RefSeq belong to Pseudomonas bacteria (Pseudomonas is only one of nearly 3,300 bacterial genera in RefSeq).

A primary concern for Treangen, an assistant professor of computer science at Rice University who is an expert in the study of genetic material from environmental samples, is maintaining the ability to quickly identify bacteria that pose a threat to public health.

Big data is uniquely positioned to do this, Treangen says, but there’s just so much of it. At present, he explains, low-cost and high-throughput DNA shotgun sequencing machines—which read short DNA sequences from collections of microorganisms—have resulted in the doubling of genomic data in RefSeq every two to three years.

“I initially thought more data is always better for these methods,” says Treangen, who worked closely with Nasko at UMIACS for several years before relocating to Rice earlier this year. “You would expect that there would be no penalty, because database growth is good.”

However, the researchers found that bacterial data in RefSeq has an outsized effect at the species level of the taxonomic hierarchy, which is growing at a breakneck pace.

That’s a problem for scientists who combine two common techniques to identify what they find. One is called k-mer-based classification, which identifies short DNA sequences from all the organisms in a bacterial sample via exact matches.

Most of the methods that have made the problem computationally feasible rely on k-mers, which are exact matches of DNA strings of length ‘k,’ says Treangen. “If a sequenced read perfectly matches something in the database, the intuition is that you can say what that is with great precision and shortcut more expensive computational approaches,” he says.

A commonly used technique with k-mer-based classification is lowest common ancestor (LCA) assignment. LCA assesses all of the organisms an unknown piece of DNA matched, assigning them if necessary to a higher level in the taxonomy, such as a genus rather than a species. But this may not be specific enough for researchers trying to pin down a pathogen.

In fact, the study by Nasko and the other researchers found a k-mer-based classification tool called Bracken, which uses Bayesian statistics to infer the best match for a sequence, which helped mitigate the imbalance. Even so, it struggled to identify genomes with close relatives, but not perfect matches, in the database.

Treangen says well-funded research into particular pathogens is a necessity and has greatly aided rapid-outbreak detection and tracking, but it ultimately biases public databases like RefSeq.

“For instance, there's an immense bias toward foodborne pathogens,” he says. “Society wants to know a lot about Salmonella, and rightfully so. The FDA, and specifically GenomeTrakr, have aided in the sequencing of thousands of relevant pathogens and have added them directly to the reference database.”

However, Treangen says, that skews the reference database toward particular genera and families of microbes in a way that affects the accuracy and sensitivity of fast taxonomic-classification tools like Kraken that use k-mer and LCA-based approaches.

Nasko says the best recent example of a false positive identification is a study that initially reported evidence of deadly bacteria in New York City’s subways. The study, based on sequenced genomes from samples, was later revised to reflect mismatches that falsely identified the sequences of Bacillus cereus (a common bacterium not harmful to humans) as Bacillus anthracis (the bacteria responsible for anthrax).

While a focus on public health is a key priority, the research team says novel techniques able to cope with database growth and noise, coupled with an increased breadth of sequenced genomes, is needed for continued improvements in the field.

“The genetic boundaries that once clearly divided certain bacteria from others are beginning to get a bit murkier as we continue to see more regions different genomes that are shared across species and genera of microbes,” Nasko says. “Our analysis demonstrates that current computational tools make searches against large databases possible, and that’s important, but more sensitive searching strategies are still needed to improve the accuracy of these DNA classifications.”

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The research was supported by the Division of Intramural Research of the National Human Genome Research Institute, part of the National Institutes of Health, and the Intelligence Advanced Research Projects Activity via the Army Research Office.

This article was adapted from a news release published by Rice University.