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Hypothesis-driven Computational Genomics: Engaging Students in Lab Protocols and Bioinformatics via Inquiry

PLEASE NOTE: PIs and Co-PIs are listed alphabetically.

Title

Hypothesis-driven Computational Genomics: Engaging Students in Lab Protocols and Bioinformatics via Inquiry

PI

Gail Rosen, Ph.D. (PI)
Assistant Professor, Electrical and Computer Engineering
College of Engineering

Co-PI(s)

Penny Hammrich, Ph.D.
Professor
School of Education

Jacob A. Russell, Ph.D.
Assistant Professor, Biology
College of Arts and Sciences

Funding

National Science Foundation logo National Science Foundation (NSF)
Early Concept Grants for Exploratory Research (EAGER)
Amount of Award: $199,993

Description

The accelerating influx of genomic, transcriptomic and proteomic data have created a large need for workforce employees with training in computer science and biology. Training in both disciplines can be well achieved through the implementation of appropriate bioinformatics curricula. Yet the course offerings and plans of study at many universities fall short in providing a well-rounded education that incorporates a focus on the analysis of large-scale, “-omic” datasets.

PI Rosen and co-PI Russell have developed training in bioinformatics at Drexel University, teaching interdisciplinary courses that aim to educate students in the realms of genomics, molecular evolution, and the bioinformatics analyses of genes and genomes. Students participating in these courses come from several majors, departments, and colleges, providing a diverse multidisciplinary environment with great potential for peer learning.

genomeTo enrich the current course offerings in bioinformatics at Drexel University, the PIs aim to develop a new class, Metagenomics, that will be integrated into a three-course bioinformatics sequence. The new course will train students in the analysis of high-throughput metagenomic datasets, while the two existing courses (Molecular Ecology Lab and Bioinformatics I) will prepare students for this more advanced course. The PIs w ill adopt a case-study -based approach for this course sequence, with students participating in a metagenomics project at the stages of hypothesis development (Molecular Ecology Lab), sample preparation (Molecular Ecology Lab), metagenome annotation (Metagenomics), and hypothesis testing (Metagenomics). While developing hypotheses, students will be trained in the basics of DNA sequence analysis in the Fall quarter (Molecular Ecology Lab and Bioinformatics). In the winter quarter they will then analyze metagenomic datasets that they helped to generate, using these to test hypotheses about microbial ecology, symbiosis, and the roles of microbes in digestion and nutrition —activities that complement co-PI Russell’s current NSF-funded research.

It is hoped that this thematic strategy will actively engage students in the learning process, helping them to develop as critical-thinkers who understand the scientific method. It is also hoped that the students will view the daunting array of required skills as a means to an end, rather than a lengthy check- off list or a series of hoops to jump through for a quality grade, and that this will positively impact their bioinformatics training. To measure the success of this approach, students will answer a series of questions about their impressions and perceived understanding of bioinformatics throughout the course sequence. They will also be tested on their knowledge of bioinformatics at the beginning and end of the sequence. Assessments of students taking all three courses, one or two of the courses, or none of the courses will be compared to determine the program’s impact. If successful, university, college, and departmental resources will be used to maintain the course sequence as an annual offering within the BIO and Engineering majors. Students taking these courses in subsequent years will analyze the original metagenomic datasets, yet they will also develop smaller, more affordable sequence datasets that will still allow them to test hypotheses and to see a project through from start to finish.

Funding of the proposed curriculum development will facilitate the training of students who are highly qualified for careers in bioinformatics, having worked with the types of datasets that bioinformaticians routinely analyze. Students from diverse backgrounds and several majors will participate, creating a multidisciplinary experience and giving students an opportunity to learn from each other through group activities. In years 2 and 3, one student will be funded to present findings from the course at a regional or national conference. Scientific findings and educational outcomes will be presented by the PIs at similar conferences and will also be published in peer-review journals. The generation and analysis of high throughput datasets will help to modernize bioinformatics education at Drexel. And the three-course sequence (and case-study based approach) will ideally provide an adoptable model for other universities. Such adoption will be facilitated through the posting of course syllabi, lectures, and problem sets online.