This lab introduces students to species concepts and basic computer-based tree-building methods using published nuclear and mitochondrial sequence data for Plethodon salamanders.

Through this 4-part bioinformatics case study, students will be led through the forensic analysis of putative Yeti artifacts based on published findings.

Introductory bioinformatics exercises often walk students through the use of computational tools, but often provide little understanding of what a computational tool does "under the hood." A solid understanding of how a bioinformatics computational algorithm functions, including its limitations, is key for interpreting the output in a biologically relevant context. This introductory bioinformatics exercise integrates an introduction to web-based sequence alignment algorithms with models to facilitate student reflection and appreciation for how computational tools provide similarity output data. The exercise concludes with a set of inquiry-based questions in which students may apply computational tools to solve a real biological problem.

In the module, students first define sequence similarity and then investigate how similarity can be quantitatively compared between two similar length proteins using a Blocks Substitution Matrix (BLOSUM) scoring matrix. Students then look for local regions of similarity between a sequence query and subjects within a large database using Basic Local Alignment Search Tool (BLAST). Lastly, students access text-based FASTA-formatted sequence information via National Center for Biotechnology Information (NCBI) databases as they collect sequences for a multiple sequence alignment using Clustal Omega to generate a phylogram and evaluate evolutionary relationships. The combination of diverse, inquiry-based questions, paper models, and web-based computational resources provides students with a solid basis for more advanced bioinformatics topics and an appreciation for the importance of bioinformatics tools across the discipline of biology.

Analyzing high-throughput DNA sequence data is a fundamental skill in modern biology. However, real and perceived barriers such as massive file sizes, substantial computational requirements, and lack of instructor background knowledge can discourage faculty from incorporating high-throughput sequence data into their courses. We developed a straightforward and detailed tutorial that guides students through the analysis of RNA sequencing (RNA-seq) data using Galaxy, a public web-based bioinformatics platform. The tutorial stretches over three laboratory periods (~8 hours) and is appropriate for undergraduate molecular biology and genetics courses. Sequence files are imported into a student's Galaxy user account directly from the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA), eliminating the need for on-site file storage. Using Galaxy's graphical user interface and a defined set of analysis tools, students perform sequence quality assessment and trimming, map individual sequence reads to a genome, generate a counts table, and carry out differential gene expression analysis. All of these steps are performed "in the cloud," using offsite computational infrastructure. The provided tutorial utilizes RNA-seq data from a published study focused on nematode infection of Arabidopsis thaliana. Based on their analysis of the data, students are challenged to develop new hypotheses about how plants respond to nematode parasitism. However, the workflow is flexible and can accommodate alternative data sets from NCBI SRA or the instructor. Overall, this resource provides a simple introduction to the analysis of "big data" in the undergraduate classroom, with limited prior background and infrastructure required for successful implementation.

A core competency identified in Vision and Change for undergraduate biology students is the Ability to Apply the Process of Science. Here, we describe a three-week laboratory module for students in an Introductory Cell and Molecular Biology course. The goal of our module is to introduce students to the critical scientific process skill of metacognition early in their undergraduate careers, which is not only important for scientific research, but also for learning new concepts and other types of problem solving. To achieve this, our laboratory module engages students in the investigation of a biological research question while specifically and explicitly prompting students to practice the metacognition regularly employed by scientists. In our research module, students gather information, generate hypotheses, evaluate the utility of different experimental approaches in testing their hypotheses, planning experiments, and analyzing data. In-class and take-home activities prompt students to actively reflect on the information they use to design their experiments and to draw their conclusions. The module has been implemented several times in recent academic years, with two or three concurrent sections of the course taking part each academic quarter. Student evaluations and interviews suggest that this module provides a meaningful introduction to metacognition as it is used in scientific problem solving. Here we present the pedagogical structure of our laboratory module, which could be adapted to engage students in investigating a wide variety of research questions.

Participation in research provides personal and professional benefits for undergraduates. However, some students face institutional barriers that prevent their entry into research, particularly those from underrepresented groups who may stand to gain the most from research experiences. Course-based undergraduate research experiences (CUREs) effectively scale research availability, but many only last for a single semester, which is rarely enough time for a novice to develop proficiency. To address these challenges, we present the Pipeline CURE, a framework that integrates a single research question throughout a biology curriculum. Students are introduced to the research system - in this implementation, C. elegans epigenetics research - with their first course in the major. After revisiting the research system in several subsequent courses, students can choose to participate in an upper-level research experience. In the Pipeline, students build resilience via repeated exposure to the same research system. Its iterative, curriculum-embedded approach is flexible enough to be implemented at a range of institutions using a variety of research questions. By uniting evidence-based teaching methods with ongoing scientific research, the Pipeline CURE provides a new model for overcoming barriers to participation in undergraduate research.

Advances in high-throughput techniques have resulted in a rising demand for scientists with basic bioinformatics skills as well as workshops and curricula that teach students bioinformatics concepts. DNA Detective is a workshop we designed to introduce students to big data and bioinformatics using CyVerse and the Dolan DNA Learning Center's online DNA Subway platform. DNA Subway is a user-friendly workspace for genome analysis and uses the metaphor of a network of subway lines to familiarize users with the steps involved in annotating and comparing DNA sequences. For DNA Detective, we use the DNA Subway Red Line to guide students through analyzing a "mystery" DNA sequence to distinguish its gene structure and name. During the workshop, students are assigned a unique Arabidopsis thaliana DNA sequence. Students "travel" the Red Line to computationally find and remove sequence repeats, use gene prediction software to identify structural elements of the sequence, search databases of known genes to determine the identity of their mystery sequence, and synthesize these results into a model of their gene. Next, students use The Arabidopsis Information Resource (TAIR) to identify their gene's function so they can hypothesize what a mutant plant lacking that gene might look like (its phenotype). Then, from a group of plants in the room, students select the plant they think is most likely defective for their gene. Through this workshop, students are acquainted to the flow of genetic information from genotype to phenotype and tackle complex genomics analyses in hopes of inspiring and empowering them towards continued science education.

Radical innovations in DNA sequencing technology over the past decade have created an increased need for computational bioinformatics analyses in the 21^st century STEM workforce. Recent evidence however demonstrates that there are significant barriers to teaching these skills at the undergraduate level including lack of faculty training, lack of student interest in bioinformatics, lack of vetted teaching materials, and overly full curricula. To this end, the James Madison University, Center for Genome & Metagenome Studies (JMU CGEMS) and other PUI collaborators are devoted to developing and disseminating engaging bioinformatics teaching materials specifically designed for streamlined integration into general undergraduate biology curriculum. Here, we have developed and integrated a fun introductory level lesson to command line next generation sequencing (NGS) analysis into a large enrollment core biology course. This one-off activity takes a crucial but mundane aspect of NGS quality control (QC) analysis and incorporates the use of Emoji data outputs using the software FASTQE to pique student interest. This amusing command line analysis is subsequently paired with a more rigorous research-grade software package called FASTP in which students complete sequence QC and filtering using a few simple commands. Collectively, this short lesson provides novice-level faculty and students an engaging entry point to learning basic genomics command line programming skills as a gateway to more complex and elaborated applications of computational bioinformatics analyses.

Primary image: Undergraduate students learn the basics of command line NGS quality analysis using the FASTQE and FASTP programs.

Developing student creativity and ability to develop a testable hypothesis represents a significant challenge in most laboratory courses. This lesson demonstrates how students use facets of molecular evolution and bioinformatics approaches involving protein sequence alignments (Clustal Omega, Uniprot) and 3D structure visualization (Pymol, JMol, Chimera), along with an analysis of pertinent background literature, to construct a novel hypothesis and develop a research proposal to explore their hypothesis. We have used this approach in a variety of institutional contexts (community college, research intensive university and primarily undergraduate institutions, PUIs ) as the first component in a protein-centric course-embedded undergraduate research experience (CURE) sequence. Built around the enzyme malate dehydrogenase, the sequence illustrates a variety of foundational concepts from the learning framework for Biochemistry and Molecular Biology. The lesson has three specific learning goals: i) find, use and present relevant primary literature, protein sequences, structures, and analyses resulting from the use of bioinformatics tools, ii) understand the various roles that non-covalent interactions may play in the structure and function of an enzyme. and iii) create/develop a testable and falsifiable hypothesis and propose appropriate experiments to interrogate the hypothesis. For each learning goal, we have developed specific assessment rubrics. Depending on the needs of the course, this approach builds to an in-class student presentation and/or a written research proposal. The module can be extended over several lecture and lab periods. Furthermore, the module lends itself to additional assessments including oral presentation, research proposal writing and the validated pre-post Experimental Design Ability Test (EDAT). Although presented in the context of course-based research on malate dehydrogenase, the approach and materials presented are readily adaptable to any protein of interest.

Primary image: Mind map of the hypothesis development.

Science is a process of discovery where failure is inherent and iteration is necessary, yet instructors often teach the scientific process as if it is a controlled, highly supervised, confirmatory practice of following directions to get a known answer. We believe this mismatch occurs because instructors often struggle to feel comfortable in facilitating open-ended inquiry and giving students the trust and autonomy to experience an authentic scientific process. In this quarter-long lab curriculum, we bring the scientific process into the classroom in the form of an authentic course-based undergraduate research experience (CURE). We present a pedagogy, which is hands-on for students and hands-off for instructors, that incorporates and celebrates the learning that occurs from failing safely and often. The research project presented in this article is a genomics-based CURE where students sequence and analyze DNA genome segments. Throughout the lesson, we present core instructional structures and techniques that are transferable to any project and help scaffold and support the learning impact of the CURE. In the following curriculum, we outline this pedagogy, applied to a model CURE focused on sequencing a bacterium, and suggest ways that both the pedagogy and the core components of our CURE (i.e., journal club, posters, lab notebook, and self-assessments) transfer to other courses, and other research projects.

Primary Image: Gita Bangera guiding Bellevue College students through the ComGen research process in a cellular biology course.

Poster on using Cyverse resources to make classroom that make using bioinformatics in the classroom a more manageable experience presented at the 2020 BIOME Institute: Cultivating Scientific Curiosity

This laboratory module, published on CourseSource, leads introductory biology students in the exploration of a basic set of bioinformatics concepts and tools.