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Promoting Climate Change Literacy for Non-majors: Implementation of an atmospheric carbon dioxide modeling activity as...Learning Objectives
- Students will be able to manipulate and produce data and graphs.
- Students will be able to design a simple mathematical model of atmospheric CO2 that can be used to make predictions.
- Students will be able to conduct simulations, analyze, interpret, and draw conclusions about atmospheric CO2 levels from their own computer generated simulated data.
Using QIIME to Interpret Environmental Microbial Communities in an Upper Level Metagenomics CourseLearning ObjectivesStudents will be able to:
- list and perform the steps of sequence processing and taxonomic inference.
- interpret microbial community diversity from metagenomic sequence datasets.
- compare microbial diversity within and between samples or treatments.
A first lesson in mathematical modeling for biologists: RocsLearning Objectives
- Systematically develop a functioning, discrete, single-species model of an exponentially-growing or -declining population.
- Use the model to recommend appropriate action for population management.
- Communicate model output and recommendations to non-expert audiences.
- Generate a collaborative work product that most individuals could not generate on their own, given time and resource constraints.
Out of Your Seat and on Your Feet! An adaptable course-based research project in plant ecology for advanced studentsLearning ObjectivesStudents will:
- Articulate testable hypotheses. (Lab 8, final presentation/paper, in-class exercises)
- Analyze data to determine the level of support for articulated hypotheses. (Labs 4-7, final presentation/paper)
- Identify multiple species of plants in the field quickly and accurately. (Labs 2-3, field trip)
- Measure environmental variables and sample vegetation in the field. (Labs 2-3, field trip)
- Analyze soil samples using a variety of low-tech lab techniques. (Open labs after field trip)
- Use multiple statistical techniques to analyze data for patterns. (Labs 4-8, final presentation/paper)
- Interpret statistical analyses to distinguish between strong and weak interactions in a biological system. (Labs 4-7, final presentation/paper)
- Develop and present a conference-style presentation in a public forum. (Lab 8, final presentation/paper)
- Write a publication-ready research paper communicating findings and displaying data. (Lab 8, final presentation/paper)
Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence...Learning ObjectivesAt the end of this lesson, students will be able to:
- Define similarity in a non-biological and biological sense when provided with two strings of letters.
- Quantify the similarity between two gene/protein sequences.
- Explain how a substitution matrix is used to quantify similarity.
- Calculate amino acid similarity scores using a scoring matrix.
- Demonstrate how to access genomic data (e.g., from NCBI nucleotide and protein databases).
- Demonstrate how to use bioinformatics tools to analyze genomic data (e.g., BLASTP), explain a simplified BLAST search algorithm including how similarity is used to perform a BLAST search, and how to evaluate the results of a BLAST search.
- Create a nearest-neighbor distance matrix.
- Create a multiple sequence alignment using a nearest-neighbor distance matrix and a phylogram based on similarity of amino acid sequences.
- Use appropriate bioinformatics sequence alignment tools to investigate a biological question.
Tackling "Big Data" with Biology Undergrads: A Simple RNA-seq Data Analysis Tutorial Using GalaxyLearning Objectives
- Students will locate and download high-throughput sequence data and genome annotation files from publically available data repositories.
- Students will use Galaxy to create an automated computational workflow that performs sequence quality assessment, trimming, and mapping of RNA-seq data.
- Students will analyze and interpret the outputs of RNA-seq analysis programs.
- Students will identify a group of genes that is differentially expressed between treatment and control samples, and interpret the biological significance of this list of differentially expressed genes.