Integrating Bioinformatics Education Series

The Life Science fields (e.g., biology, botany, zoology, microbiology, physiology, biochemistry, and related subjects) are rapidly being transformed into data driven disciplines due to enormous changes in the volume and variety of data and computing capabilities. To help ensure that Arkansas Life Science majors are prepared for these changes, the AR INBRE Bioinformatics Core (Supported by a grant from NIGMS (P20 GM103429) at NIH) is sponsoring an on-going education series for Life Science faculty to share their insights, experiences, and course materials for integrating bioinformatics content into their curricula.

Fall 2020 Seminar Dates – All sessions will be hosted by Blackboard Collaborate using this link: In addition, the recorded sessions will be made available on the AR INBRE Youtube Channel for later viewing – .

Friday, September 4 at 3:00 pm CST Recorded Session available at
Speaker Topic
David Ussery grew up in Springdale, Arkansas. He got his Ph.D. in biochemistry from the University of Cincinnati College of Medicine, and did a post-doc at Oxford University. He has lived in Denmark for 15 years, before returning to Arkansas, where he is the Helen Adams & the Arkansas Research Alliance Chair in Biomedical Informatics at the University of Arkansas for BioMedical Sciences. He has worked with genomics for more than 20 years, and is currently working on several projects related to microbial genomics, including SARS-CoV-2. His work combines high-throughput experimental and computational methods, in order to aid in rapid diagnostics. He is co-lead in the ‘Data-Life Cycle and Curation’ part of the NSF-EPSCoR track 1 grant for the state of Arkansas and Director of the ARC-GEM Center ( Dr. Ussery will talk about some of his recent work on comparative genomics of SARS-CoV-2. There are now close to 100,000 coronavirus genomes available, with the number growing daily. For faculty and students working on big bio-data research projects, data curation is an essential part of working with big data, especially messy biological data, as shown in the figure below, where the ‘data curation’ can be thought of as a washing machine, that cleans the data for further use. ARC-GEM Data Washing Machine.
Friday, September 11 at 3:00 pm CST Recorded Session available at
Speaker Topic
Dr. Phil H Williams is the Bioinformatics Technical Director at the MidSouth Bioinformatics Center located at the University of Arkansas at Little Rock (UALR). This is a core facility for the joint UALR, University of Arkansas for Medical Sciences (UAMS) bioinformatics program. Phil received his PhD from the joint UALR, UAMS bioinformatics program. He did a postdoc at the Australian National University in Canberra. His research interest are in function prediction for long-noncoding RNA (lncRNA) as well as interaction between microRNAs and lncRNA. The relationship between epigenetics, retrotransposons, lncRNA and evolution are also of interest. Phil has applied machine learning to several data types including genomic and mass spec. Dr. Phil Williams will show faculty how to use Galaxy, an open source, web-based platform for data intensive biomedical research ( With this graphical web tool bioinformatics data processing can be performed without programming experience. This session starts by showing people how to bring data and bioinformatics tools together in the Galaxy workspace. Tools for trimming and quality control of next-generation sequence reads are demonstrated. In the second half of the session, proteomic data will be used to demonstrate how to upload your own data into Galaxy. Proteomic data analysis tools will be demonstrated using the data. The advantages of Galaxy is that No programming experience needed and a Server is not required. The downsides of Galaxy include: the server may be down, you may have to wait for processing to complete, and there is limited data storage
Friday, September 18 at 3:00 pm CST Recorded Session available at
Speaker Topic
Mr Jeff Pummill is the co-Director of the Arkansas High Performance Computing Center (AHPCC) at UofA Fayetteville and holds an Adjunct position in the department of Biological Sciences. As the first employee of the newly created center in 2005, he served as both administrator and user services support person from 2005-2010 during which time AHPCC fielded two consecutive Top500 HPC systems. His current emphasis areas now include enabling computational tools for researchers, pairing projects with the appropriate hardware and software resources, contributing skills and effort to funded projects in the area of computational science, providing support for grant efforts, developing educational courses and workshops, and participating as a graduate committee member when a scientific computing component is desirable. Enhancing and Augmenting your Bioinformatics Offerings with AHPCC Resources. The Arkansas High Performance Computing Center (AHPCC) provides expertise, high performance computing hardware, storage, support services, and training to enable computationally-intensive and data-intensive research. The AHPCC ( is available to faculty, staff and students at all of the Arkansas public universities, and to their collaborators inside and outside of the state. Most AHPCC services are provided free of charge to eligible researchers.
Friday, September 25 at 3:00 pm CST Recorded Session available at
Speaker Topic
When Dr. William Grisham was an undergraduate, he wanted to change the world. Now, as a UCLA professor, he has combined the resources of UCLA’s Office of Instructional Development, the UCLA Psychology Department, and the National Science Foundation to make his dream come true. Dr. Grisham is taking the high-quality laboratory experiences that he offers his students at UCLA and transforming them so that they are completely digital. Further, he has provided these digital teaching tools on the web so that they can be downloaded for free from Students often seem unaware of the bioinformatics resources available to them. The Bioinformatics unit that we devised is a tour-de-force weaving together resources such as the Mouse Brain Library, WebQTL, UCSC Genome Browser, Uniprot, The Allen Brain Atlas, Gene, and Pubmed. Students start with quantifying a phenotype in recombinant inbred mice, finding the chromosomal locus where there are likely genes that affect the phenotype, finding candidate genes, determining if these genes are expressed in the tissue of interest, determining the cellular loci in which these genes are expressed, determining the coding sequence, and finding articles relating to their candidate gene. Along the way, they learn about the techniques that allowed these resources to be constructed. Surprisingly, all of this can be accomplished in about three afternoons.
Friday, October 2 at 1:00 pm CST Recorded Session available at
Speaker Topic
Dr. Anne Rosenwald: I earned my PhD in Biochemistry from the Johns Hopkins University School of Public Health. After post-doctoral fellowships at the Carnegie Institution of Washington and the National Cancer Institute, I took a position in the Department of Biology at Georgetown University where I am now a full professor, with a joint appointment in the Department of Microbiology and Immunology at the Georgetown University Medical Center. My laboratory’s research involved membrane traffic, ion homeostasis, and cell wall metabolism in the model organism, Saccharomyces cerevisiae and it’s pathogenic relative, Candida glabrata. Beginning in 2010, my interests began to shift from wet-lab work to undergraduate science education, especially in the field of bioinformatics and data science. As part of these endeavors, I helped to develop the Genome Solver Project. I am also a member of several other national organizations devoted to bringing genomics and bioinformatics to undergraduate classrooms, including the Genomics Education Partnership, the Network for Integrating Bioinformatics into Life Sciences Education, and most recently, the Genomics Education Alliance. Genome Solver began as a project to help train faculty with little experience in bioinformatics. Beginning in 2012, we began delivering face-to-face workshops for faculty, first at the J. Craig Venter Institute in Rockville, MD, and then at colleges and universities around the country. I will discuss the evolution of the project over time, with emphasis on our current efforts, including our community science project. The current Genome Solver team is me and two former postdoctoral fellows, Gaurav Arora at Gallaudet University and Vinayak Mathur at Cabrini University. All of our materials are available under a Creative Commons license at
Friday, October 9 at 3:00 pm CST Recorded Session available at
Speaker Topic
Dr. David Donley is an Assistant Professor in the Harding University Department of Biology. By training, Dr. Donley is a neuroscientist and molecular biologist with research interests in neurological diseases and neuroinflammation. Dr. Donley’s lab is focused on providing high-quality research experiences to undergraduate students. Currently, his lab studies the inflammatory response of microglia to disease-relevant proteins such as beta-amyloid-42. Traditionally, Dr. Donley has primarily used techniques such as qPCR, flow cytometry, functional enzymatic assays, and microscopy. Increasingly, his lab uses bioinformatic and computational tools to expand the research experience and support student interests. He currently teaches a variety of classes and labs at Harding University ranging from General Biology to Molecular and Cellular Neurobiology. Many undergraduate students view bioinformatics as a mysterious field that is too complicated to understand. Dr. Donley will discuss how his lab is using proteomic data to involve undergraduate students in research. He will describe multiple approaches for using large data sets to promote student engagement in research experiences. His talk will also discuss how to reduce barriers to entry for undergraduates and how to help students gain confidence in computational strategies.
Friday, October 16 at 2:00 pm CST Recorded Session available at
Speaker Topic
Dr. Hong Qin is an Associate Professor in the Department of Computer Science and Engineering at the University of Tennessee – Chattanooga who uses computational and mathematical approaches to investigate biomedical and biological questions. One focus is to develop probabilistic gene network models to infer network changes during cellular aging. We build gene network models from heterogeneous genomics data sets, including protein interactions, gene expression data sets, RNAseq data sets, protein mass-spec data sets, high-throughput phenotypic screens, and gene annotations. We are developing machine-learning methods to automatically estimate cellular lifespan from time-lapsed images. We are also applying engineering principles to study molecular, biological, and ecological networks. We are developing deep-learning methods for better classification and prediction using heterogeneous biomedical and biological large data sets. Dr. Hong Qin is a recipient of a NSF CAREER award 2015-2020. Qin’s expertise: Graph reliability modeling; Bioinformatics; Computational genomics; Mathematical modeling; Systems Biology; Cellular aging; Gene network analysis and modeling Dr. Qin will present how to use R to analyze COVID 19 data. R (along with bio-python and bio-perl) is one of the top choices for analyzing life science data. R is open source, runs on multiple platforms (Windows, Linux, MacOS, and cloud), has excellent packages for analyzing genomic data (e.g., Bioconductor), and has several nice interfaces for developing and running code (R Studio and Jupyter Notebook). The R code designed for this demo is available from This tutorial runs in Google’s CoLab cloud so no local installation is needed.
Friday, October 23 at 3:00 pm CST Recorded Session available at
Speaker Topic
Dr. April Bednarski is a curriculum specialist and instructor for the undergraduate biology program at Washington University in St Louis. April’s research interests are in creating authentic course-based research experiences for undergraduates (CURES). She started this work as a Howard Hughes Biology Education Postdoctoral Fellow in Dr. Sally Elgin’s lab, also at Washington University. She worked with Dr. Elgin to develop the Genomics Education Partnership and bioinformatics curriculum for undergraduates for use in the large introductory lecture courses. She has continued that work and currently teaches a CURE called Enzyme Analysis, facilitating students in designing and carrying out their own semester-long research projects centered around site-directed mutagenesis. The projects also use computational methods to model protein structure and small-molecule docking. April received her BA in Biochemistry from the University of Iowa and her PhD in Medicinal Chemistry from the University of Michigan. Incorporating Bioinformatics in Undergraduate Biology Education from Freshman to Senior Year. Her talk will describe curriculum projects for freshman and sophomore students that use bioinformatics tools to illustrate principles of central dogma and use of model organisms. She will then describe a course-based research experience that builds on that knowledge in junior and senior years to provide opportunities for students to ask their own research questions, develop hypotheses, and design experiments.
Friday, October 30 at 2:00 pm CST Recorded Session available at
Speaker Topic
Dr. W. Kelley Thomas directs UNH’s Hubbard Center for Genome Studies, a leader in comparative and environmental genomics with a special emphasis on novel model species. Dr. Thomas is interested in using molecules to understand the relationships among organisms and between organisms and their environment. Increasing our understanding of the mechanisms of molecular change and developing a critical link between molecular and organismal evolution is a key component to his research programs. Current research in his lab also focuses on the response of organisms to environmental change and genome evolution using various DNA sequencing technologies and bioinformatics. Throughout my career, I have demonstrated two over-arching characteristics. The first is that I have always worked at a disciplinary interface. From my undergraduate field biology projects using the tools of protein biochemistry to study kangaroo rat populations, to my current work applying and innovating tools to study genetic errors and biodiversity. I have exploited a multidisciplinary perspective as the director of the Hubbard Center for Genome Studies at UNH where I have been instrumental in bringing DNA sequencing technologies to our faculty, especially those working in the field(s) of Ecology and Evolutionary Biology. Eight years ago, the HCGS became involved with our NH-INBRE program in collaboration with the lead institution, current PI William Green, Dartmouth College). I am now the director of the Bioinformatics and Genomics Core and PI for the University of New Hampshire of that very successful NIH-sponsored program. My role is to support genomics and bioinformatics research and training at our primarily undergraduate and lead institutions in New Hampshire. Most recently, I have become focused on the need for broad-based, effective training in bioinformatics and rigor and reproducibility. Based on the successful development of curriculum support modules for NH-INBRE, we have established a training workshop using the IPERT mechanism (through MDIBL). The week-long workshop called “Train the Trainers” facilitates the integration of bioinformatics into the undergraduate curriculum.
Friday, November 6 at 3:30 pm CST Due to unexpected circumstances, this session is cancelled. Dr. Wilson will present at a later date.
Speaker Topic
Dr. Melissa Wilson is a computational evolutionary biologist whose main research interests include sex-biased biology. She studies the evolution of sex chromosomes (X and Y in mammals), why mutation rates differ between males and females, and how changes in population history affect the sex chromosomes differently than the non-sex chromosomes. Generally she studies mammals, but is also curious about the sex-biased biology of flies, worms and plants. Professor Wilson is also active in public science engagement and outreach. She routinely teaches in K-12 classrooms, and regularly engages the public in discussions about the difference between sex and gender, the importance (or not) of genetic inheritance, and understanding evolution. She teaches evolution and computational biology at Arizona State University. Dr. Wilson will talk about bioinformatics education as well as demonstrating how to teach students how to use bioinformatics in an online environment.
Friday, November 13 at 2:00 pm CST Recorded Session available at
Speaker Topic
Dr. Sunghwan Kim is a Staff Scientist at the National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), U.S. National Institutes of Health (NIH). As a computational chemist and cheminformatician, he works on the PubChem project, which develops and maintains a chemical database called PubChem ( His major contribution to PubChem is the development and improvement of “PubChem3D,” which is PubChem’s chemical information resource derived from 3-dimensional (3-D) molecular structures. He is also involved in various outreach and educational activities that aim to help the public better understand PubChem’s data/tools/services. He holds a M.Sc. in Inorganic Chemistry (from Hanyang University, South Korea) and a Ph.D. in Physical Chemistry (from the University of Georgia at Athens). Dr. Kim will talk about PubChem ( and its application for cheminformatics education. PubChem is a popular chemical information resource, visited by millions of users per month. Considering that many PubChem users are young students, PubChem has great potential as an education resource. In this talk, Dr. Kim will provide a brief overview of cheminformatics and explain PubChem’s data/tools/services that can be used to teach informatics skills for college students. This includes keyword search (e.g. using chemical names, gene/protein/pathway names) structure search (e.g., identity, 2D/3D similarity search, substructure/superstructure search), laboratory chemical safety sheet (LCSS), automated data access using computer scripts, building predictive modes for bioactivity of molecules and so on.
Friday, November 20 at 3:00 pm CST Recorded Session available at
Speaker Topic
Dr. Nathan Reyna is Associate Professor of Biology at Ouachita Baptist University. Research in his lab has two foci and utilizes multiple techniques, including cell culture, Quantitative Real Time PCR (Q-RT-PCR), RNA sequencing, and bioinformatics. One research focus is in understanding how biologically-inspired nano-structures can serve as an extracellular matrix for neuron growth and development. Currently, we are looking at the role exosomes play in neuron differentiation. The second research focus of his lab is on the use of Synthetic Biology to examine bioinformatically-identified regulatory genetic sequences. As increasing amounts of bioinformatic software are developed, each with a unique algorithm utilized for prediction, more discrepancy between putative results occurs when examining novel genomes. By physically validating predicted results we can provide insight into effective utilization of bioinformatic methods and comparative genomics. Additionally, this work serves to generate new Bio-Bricks that can be used in other Synthetic Biology projects. Since research is an extension of the classroom, his research is also incorporated into each of his courses through the use of Course-based Undergraduate Research Experiences (CURE). Flipping the Script: A Data Analysis First Approach to Incorporating Bioinformatics into the Classroom. While many tools are available for “Omics” analysis, their code-intensive environment limits our ability to incorporate them into the classroom. Traditional bioinformatics approaches that require students to learn programming languages before they begin data analysis often leaves students frustrated and uninterested in the field. However, by taking a student-focused pedagogical approach that utilizes graphical interfaces, we can reduce computational barriers. As a result, students can conduct bioinformatics research earlier in their college careers and are more likely to develop advanced computational skills in the future.This approach is scalable and can be modifiable to fit a range of classes (freshman to senior). Over the last several years, we have developed class-based and independent research projects using DNA barcoding, viral gene annotation, transcriptome, and proteomic data analysis. These projects have led to the publication of bioinformatic-oriented research articles with undergraduate first authors. The recent incorporation of data mining techniques using open-source genomic and proteomic databases, such as cBioPortal, has further increased our bioinformatics capabilities. I will present examples of how and where we have incorporated these techniques into the curriculum at Ouachita Baptist University.

OBU Bioinformatic related publications