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  • Multiple sequence alignment of homologous cytochrome C protein sequences using Jalview viewer.

    Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence...

    Learning Objectives
    At 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.
  • blind cave fish
  • Possible implementations of a short research module

    A Short Laboratory Module to Help Infuse Metacognition during an Introductory Course-based Research Experience

    Learning Objectives
    • Students will be able to evaluate the strengths and weaknesses of data.
    • Students will be able to employ prior knowledge in formulating a biological research question or hypothesis.
    • Students will be able to distinguish a research question from a testable hypothesis.
    • Students will recognize that the following are essential elements in experimental design: identifying gaps in prior knowledge, picking an appropriate approach (ex. experimental tools and controls) for testing a hypothesis, and reproducibility and repeatability.
    • Students will be able to identify appropriate experimental tools, approaches and controls to use in testing a hypothesis.
    • Students will be able to accurately explain why an experimental approach they have selected is a good choice for testing a particular hypothesis.
    • Students will be able to discuss whether experimental outcomes support or fail to support a particular hypothesis, and in the case of the latter, discuss possible reasons why.
  • “Quantifying variation in biodiversity” Groundhogs (Marmota monax) with conspicuous variation awaiting measurements.

    Teaching Biodiversity with Museum Specimens in an Inquiry-Based Lab

    Learning Objectives
    Students completing this lab module will:
    • Learn how to appropriately handle and measure museum specimens.
    • Develop the necessary statistical skills to analyze museum specimen data.
    • Become familiar with how to search an online museum database and integrate supplemental data with their own dataset.
    • Strengthen scientific communication skills by presenting research to their peers.
    • Demonstrate ability to investigate scientific questions and address obstacles that occur during data collection and integration.
    • Increase proficiency in managing and using large datasets for scientific research.
    • Make connections between natural history knowledge and morphology of organisms in developing and testing hypotheses.
  • Plant ecology students surveying vegetation at Red Hills, CA, spring 2012.  From left to right are G.L, F.D, A.M., and R.P.  Photo used with permission from all students.

    Out of Your Seat and on Your Feet! An adaptable course-based research project in plant ecology for advanced students

    Learning Objectives
    Students 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)