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  • MA plot of RNA-seq data. An MA plot is a visual summary of gene expression data which identifies genes showing differential expression between two treatments.

    Tackling "Big Data" with Biology Undergrads: A Simple RNA-seq Data Analysis Tutorial Using Galaxy

    Learning 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.
  • 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)
  • Using QIIME to Interpret Environmental Microbial Communities in an Upper Level Metagenomics Course

    Learning Objectives
    Students 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.
  • The Roc is a mythical giant bird of prey, first conceived during the Islamic Golden Age (~8th to 13th centuries CE), popularized in folk tales gathered in One Thousand One Nights. Rocs figured prominently in tales of Sinbad the Sailor. In this 1898 illustration by René Bull, the Roc is harassing two of Sinbad’s small fleet of ships. Illustration by René Bull is licensed under CC BY 2.0. (Source:

    A first lesson in mathematical modeling for biologists: Rocs

    Learning 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.
  • Reprinted by permission from Macmillan Publishers Ltd.

    A Hands-on Introduction to Hidden Markov Models

    Learning Objectives
    • Students will be able to process unannotated genomic data using ab initio gene finders as well as other inputs.
    • Students will be able to defend the proposed gene annotation.
    • Students will reflect on the other uses for HMMs.
  • Abelson kinase signaling network. The image shows many connections between genes and illustrates that signaling molecules and pathways function within networks. It emphasizes the indispensability of computational tools in understanding the molecular functioning of cells. The image was generated with Cytoscape from publicly accessible protein-protein interactions databases.

    Investigating Cell Signaling with Gene Expression Datasets

    Learning Objectives
    Students will be able to:
    • Explain the hierarchical organization of signal transduction pathways.
    • Explain the role of enzymes in signal propagation and amplification.
    • Recognize the centrality of signaling pathways in cellular processes, such as metabolism, cell division, or cell motility.
    • Rationalize the etiologic basis of disease in terms of deranged signaling pathways.
    • Use software to analyze and interpret gene expression data.
    • Use an appropriate statistical method for hypotheses testing.
    • Produce reports that are written in scientific style.