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  • 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.
  • Normal Arabidopsis plants (A) have flat, spatula shaped leaves. asymmetric leaves2 (as2) mutant plants (B) have leaves that are curled under and slightly twisted. asymmetric leaves1(as1) mutant plants (C) have leaves that are curled under and twisted but also have reduced petioles.  In the laboratory activities I present, students analyze the sequence of the as1 and as2 alleles and computationally model the wild-type and mutant proteins. Visualizing the 3-D structure of the proteins helps students understan

    Using computational molecular modeling software to demonstrate how DNA mutations cause phenotypes

    Learning Objectives
    Students successfully completing this lesson will:
    1. Practice basic molecular biology laboratory skills such as DNA isolation, PCR, and gel electrophoresis.
    2. Gather and analyze quantitative and qualitative scientific data and present it in figures.
    3. Use bioinformatics to analyze DNA sequences and obtain protein sequences for molecular modeling.
    4. Make and analyze three-dimensional (3-D) protein models using molecular modeling software.
    5. Write a laboratory report using the collected data to explain how mutations in the DNA cause changes in protein structure/function which lead to mutant phenotypes.
  • 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.
  • photo credit John Friedlein. Author (SRB) helps a student troubleshooting RStudio in the workshop session of class.
  • Using phylogenetics to make inferences about historical biogeographic patterns of evolution.

    Building Trees: Introducing evolutionary concepts by exploring Crassulaceae phylogeny and biogeography

    Learning Objectives
    Students will be able to:
    • Estimate phylogenetic trees using diverse data types and phylogenetic models.
    • Correctly make inferences about evolutionary history and relatedness from the tree diagrams obtained.
    • Use selected computer programs for phylogenetic analysis.
    • Use bootstrapping to assess the statistical support for a phylogeny.
    • Use phylogenetic data to construct, compare, and evaluate the role of geologic processes in shaping the historical and current geographic distributions of a group of organisms.
  • Modeling the Research Process: Authentic human physiology research in a large non-majors course

    Learning Objectives
    Students will be able to:
    • Read current scientific literature
    • Formulate testable hypotheses
    • Design an experimental procedure to test their hypothesis
    • Make scientific observations
    • Analyze and interpret data
    • Communicate results visually and orally
  • Human karyotype

    Homologous chromosomes? Exploring human sex chromosomes, sex determination and sex reversal using bioinformatics...

    Learning Objectives
    Students successfully completing this lesson will:
    • Practice navigating an online bioinformatics resource and identify evidence relevant to solving investigation questions
    • Contrast the array of genes expected on homologous autosomal chromosomes pairs with the array of genes expected on sex chromosome pairs
    • Use bioinformatics evidence to defend the definition of homologous chromosomes
    • Define chromosomal sex and defend the definition using experimental data
    • Investigate the genetic basis of human chromosomal sex determination
    • Identify at least two genetic mutations can lead to sex reversal
  • Fully annotated mitochondrial genome of a lichenized fungal species (Cladonia subtenuis).  This represents a visual representation of the final project result of the lesson plan. Students will submit their annotation to NCBI (GenBank) and upon acceptance of their annotation, they typically add this publicly available resource into their resume.

    A CURE-based approach to teaching genomics using mitochondrial genomes

    Learning Objectives
    • Install the appropriate programs such as Putty and WinSCP.
    • Navigate NCBI's website including their different BLAST programs (e.g., blastn, tblastx, blastp and blastx)
    • Use command-line BLAST to identify mitochondrial contigs within a whole genome assembly
    • Filter the desired sequence (using grep) and move the assembled mitochondrial genome onto your own computer (using FTP or SCP)
    • Error-correct contigs (bwa mem, samtools tview), connect and circularize organellar contigs (extending from filtered reads)
    • Transform assembled sequences into annotated genomes
    • Orient to canonical start locations in the mitochondrial genome (cox1)
    • Identify the boundaries of all coding components of the mitochondrial genome using BLAST, including: Protein coding genes (BLASTx and tBLASTX), tRNAs (proprietary programs such as tRNAscan), rRNAs (BLASTn, Chlorobox), ORFs (NCBI's ORFFinder)
    • Deposit annotation onto genome repository (NCBI)
    • Update CV/resume to reflect bioinformatics skills learned in this lesson
  • 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.
  • 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)
  • Students using the Understanding Eukaryotic Genes curriculum to construct a gene model. Students are working as a pair to complete each Module using classroom computers.

    An undergraduate bioinformatics curriculum that teaches eukaryotic gene structure

    Learning Objectives
    Module 1
    • Demonstrate basic skills in using the UCSC Genome Browser to navigate to a genomic region and to control the display settings for different evidence tracks.
    • Explain the relationships among DNA, pre-mRNA, mRNA, and protein.
    Module 2
    • Describe how a primary transcript (pre-mRNA) can be synthesized using a DNA molecule as the template.
    • Explain the importance of the 5' and 3' regions of the gene for initiation and termination of transcription by RNA polymerase II.
    • Identify the beginning and the end of a transcript using the capabilities of the genome browser.
    Module 3
    • Explain how the primary transcript generated by RNA polymerase II is processed to become a mature mRNA, using the sequence signals identified in Module 2.
    • Use the genome browser to analyze the relationships among:
    • pre-mRNA
    • 5' capping
    • 3' polyadenylation
    • splicing
    • mRNA
    Module 4
    • Identify splice donor and acceptor sites that are best supported by RNA-Seq data and TopHat splice junction predictions.
    • Utilize the canonical splice donor and splice acceptor sequences to identify intron-exon boundaries.
    Module 5
    • Determine the codons for specific amino acids and identify reading frames by examining the Base Position track in the genome browser.
    • Assemble exons to maintain the open reading frame (ORF) for a given gene.
    • Define the phases of the splice donor and acceptor sites and describe how they impact the maintenance of the ORF.
    • Identify the start and stop codons of an assembled ORF.
    Module 6
    • Demonstrate how alternative splicing of a gene can lead to different mRNAs.
    • Show how alternative splicing can lead to the production of different polypeptides and result in drastic changes in phenotype.
  • 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.
  • 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.