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Mihai Pop, a professor of computer science and the director of the University of Maryland Institute for Advanced Computer Studies (UMIACS), is being recognized for his significant achievements in computational biology and bioinformatics.

The International Society for Computational Biology (ISCB) has selected Pop as a Fellow for 2022. He joins 10 other ISCB members who have distinguished themselves through outstanding contributions to the field.

ISCB selected Pop for his leadership in the development of algorithms for analyzing metagenomic data, particularly in the context of metagenome assembly and identification of structural variants in assembly graphs, and for his important contributions to large community projects.

Much of Pop’s research focuses on the analysis of microbial communities—a scientific field called metagenomics—which involves the application of bioinformatics tools to study the genetic material from environmental, uncultured microorganisms. Recent years have seen a “metagenomic revolution,” made possible by rapid advances in sequencing technologies, Pop says.

His lab, which is part of the Center for Bioinformatics and Computational Biology, has developed a number of software tools that are now widely used in the field.

They’re currently being funded by the National Institutes of Health to develop a suite of computational tools for reconstructing DNA segments.

The $2.6 million award is supporting Pop’s work building software and developing algorithms that can reconstruct nearly-complete microbial genomes from complex mixtures found in the human gut. He says that ultimately this will help scientists better understand the role of certain microbes in human health and disease.

Pop also leads efforts to assemble data at the Human Microbiome Project, which is aimed at surveying the complex microbial communities inhabiting the human body. His 122 publications have been cited(link is external) more than 38,000 times.

The 2022 Fellows will be recognized at the Intelligent Systems for Molecular Biology conference, held this year from July 10–14 in Madison, Wisconsin.

—Story by Maria Herd

Caption: Associate Professor Rob Patro (left in photo) and Ph.D. student Dongze He (right) have released a toolkit for the efficient processing of single-cell and single-nucleus RNA sequencing data.

Rapid improvements in cell sequencing technologies in the last decade have provided clinicians and scientists with many valuable insights—from better treatment options for patients with heart disease and cancer to a much deeper understanding of how certain pathogens can affect plants and animals.

In particular, the exponential growth of high-throughput single-cell and single-nucleus RNA-sequencing technologies (collectively, single-cell transcriptomics technologies) have produced a wealth of new data. In fact, single-cell transcriptomic data constitutes the most ubiquitous components of single-cell multi-omics data, which was selected as the “2019 Technology of the Year” by the journal Nature Methods.

These technologies enable scientists to measure gene expression at the resolution of individual cells for tens or even hundreds of thousands of cells at a time. The measured gene expression can act as a crucial signal in understanding biological processes, disease progression, and even informing potential patient treatment options.

The result of this unprecedented resolution is that one can infer gene expression changes in all kinds of interesting biological contexts: How does gene expression differ between cells that respond to a drug versus those that are treatment resistant? How does gene expression differ among closely related cell types that happen to inhabit the same tissues within the body?

Single-cell sequencing has been a revolutionary tool in answering these kinds of questions.

But scientists must first “pre-process” this RNA-sequencing data—a crucial step that involves going from the raw sequencing data to a specific count of how abundant each gene is within each cell. And while there is popular commercial software available to accomplish this task, it is both time-consuming and memory intensive, as well as closed source.

Now, a multi-institutional team of researchers—including four with ties to the University of Maryland—has developed an accurate, computationally efficient, and lightweight toolkit for processing large amounts of raw single-cell and single-nucleus RNA sequencing data.

Their free suite of tools, called alevin-fry, is detailed in a paper published March 10 in Nature Methods.

“As the number and scale of single-cell, including single-nucleus, RNA-sequencing experiments grow, so do the costs associated with the processing of this data,” says Dongze He, a third-year doctoral student in computational biology at UMD and lead author on the paper. “Alevin-fry provides researchers an accurate, flexible and convenient way to process a multitude of types of single-cell data, simplifying and speeding up analysis and reducing computational costs of various single-cell related scientific activities.”

Instead of hours of processing time often requiring server-scale computers with large amounts of memory, the researchers say that their open-source toolset can process very large sets of single-cell data in only tens of minutes, using amounts of processing power and memory that is commonly available on commodity desktops and laptops, while retaining accurate results.

This exciting advancement is tied to a series of lightweight algorithms and efficient data structures, as well as a highly tuned implementation that can effectively make use of many processing threads at the same time. Applied in unison, these allow indexing a large amount of reference sequence—critical parts of the underlying genome—in small space, and quickly and accurately inferring the gene from which each sequencing read was generated.

The alevin-fry toolkit applies these lightweight approaches in a way that provides accurate results and should allow computational analysis to keep pace with the quickly-advancing biotechnology, says Rob Patro, an associate professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies.

“We think our software provides a compelling option for scientists working with single-cell and single-nucleus RNA-seq data, by enabling accurate and flexible gene quantification at a low computational cost,” Patro says. “As we receive feedback and input from other scientists using this method, we expect our software suite to grow in capability and comprehensiveness. Ultimately, we want this to be available to anyone looking for a seamless, efficient tool for advancing new discoveries using single-cell data.”

Other researchers working on the project are Mohsen Zakeri, who earned his doctorate in computer science from UMD in 2021 and is now a postdoctoral researcher at Johns Hopkins University; Hirak Sarkar, who earned his doctorate in computer science from UMD in 2020 and is now a postdoctoral researcher at Harvard Medical School; Charlotte Soneson, a research associate at the Friedrich Miescher Institute for Biomedical Research in Switzerland; and Avi Srivastava, a postdoctoral researcher at the New York Genome Center.

—Story by Melissa Brachfeld

A University of Maryland computational biologist has received seed funding for an interdisciplinary project that uses machine learning to help untangle the myriad of causes behind hearing loss.

Michael Cummings, a professor of biology with an appointment in the University of Maryland Institute for Advanced Computer Studies, is co-PI on a $150,000 grant from the UMD Brain and Behavior Institute (BBI).

The project was one of five that received BBI seed funding this year as part of an interdisciplinary program focused on generating novel tools and approaches to understand complex behaviors produced by the human brain.

Cummings, who is also director of the Center for Bioinformatics and Computational Biology, will collaborate on the project with Matthew Goupell, a professor in the Department of Hearing and Speech Sciences and director of the Auditory Perception and Modeling Lab.

“I’m thankful for this opportunity to further our understanding of age-related hearing loss, and for this seed grant that makes our initial collaboration with the Goupell lab possible," Cummings says.

The UMD team is focused on the causes of sensory and cognitive impairment that can rapidly increase with age.

Up to 40 percent of individuals older than 70 suffer from hearing and vision loss, Cummings says, and without the ability to communicate effectively, can become reclusive, frustrated and depressed.

In addition, about 25 percent of Americans older than 65 have some mild cognitive decline and 10 percent have Alzheimer’s disease.

Although each of these impairments have been studied individually, much less is known about the relationship between hearing, mild cognitive impairment, and dementia due to Alzheimer’s disease or other causes, except that there is growing evidence of a link.

To understand how aging affects our sensory and cognitive systems, the UMD team believes it imperative to decipher the joint role of hearing and speech understanding ability, the neural mechanisms that contribute to age-related declines in these areas, and what role hearing plays in age-related cognitive impairment and dementia due to Alzheimer’s disease or other causes.

Their study will use machine learning on a substantial existing database of behavioral, clinical and cognitive data that have been previously collected to generate results for subsequent research.

“The problem is very complex when you consider the relationship between aging, sensation and cognition in individual people,” Goupell says. “While we have known for years that there is a correlation between hearing loss and cognitive decline, we have not made much progress on understanding why this occurs. Machine learning will help guide us to the most probable answers, particularly ones that we may not have considered yet, which will allow us to design future studies with strong testable hypotheses.”

—Story by Melissa Brachfeld

Jamshed Khan, a third-year computer science doctoral student, recently received the Ian Lawson Van Toch Memorial Award for Outstanding Student Paper at the 2021 Conference on Intelligent Systems for Molecular Biology (ISMB) and European Conference on Computational Biology (ECCB).

ISMB is the flagship meeting of the International Society for Computational Biology (ISCB). The event is co-located with ECCB and has grown to become the world’s largest bioinformatics and computational biology conference. The conference was held virtually July 25–30 due to the ongoing COVID-19 pandemic.

The award went to “Cuttlefish: Fast, parallel, and low-memory compaction of de Bruijn graphs from large-scale genome collections,” authored by Khan and his adviser Rob Patro, an associate professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). The paper introduces a new algorithm for efficiently building a compacted de Bruijn graph from collections of input genome or transcriptome sequences.

The de Bruijn graph and its colored compacted variant has become an increasingly useful data structure in genomic analysis. The structure is used to assemble new genomes, compare the genomes of related organisms, and build efficient indexes for mapping or aligning the data from sequencing experiments, among other tasks.

However, the process of building the compacted de Bruijn graph is a computational challenge, with many existing solutions requiring a lot of time and memory as the input becomes larger.

In the paper, Khan and Patro—who are both members of the Center for Bioinformatics and Computational Biology (CBCB)—present a highly scalable and very low-memory algorithm called Cuttlefish, to construct the compacted de Bruijn graph for collections of whole genome references.

The researchers say Cuttlefish considerably outperforms existing state-of-the-art approaches. It can build the compacted de Bruijn graph for 100 human genomes two and half times as fast and using less than a quarter of the memory compared to the next best tool performing the same task.

“By providing a faster and more memory-frugal algorithm for constructing this widely-used structure of the compacted de Bruijn graph, we anticipate that Cuttlefish will aid in some of the many downstream applications in which this graph is used, like comparative genomics, pangenome analysis, and the indexing of large collections of related genomes for sequence analysis,” Patro says.

The Ian Lawson Van Toch Memorial Award for Outstanding Student Paper is given to the student who presents the most thought-provoking or original paper at ISMB/ECCB, as judged by a panel of experts. The award, which is sponsored by the Princess Margaret Hospital Foundation, is given in memory of Ian Lawson Van Toch, a 23-year-old medical biophysics graduate student at the University of Toronto who passed away in August 2007.

Khan says he is honored to receive such a prestigious award.

“We are ecstatic for this recognition of our work,” he says. “I'm very grateful to Rob for his support and guidance along the way, and for being such a remarkable adviser and mentor. I'm also thankful to my talented and dedicated lab-mates for their critiques and feedback. We look forward to Cuttlefish paving the way toward more exciting results in high-throughput genomics research.”