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Bioinformatics

  • Using Undergraduate Molecular Biology Labs to Discover Targets of miRNAs in Humans

    Learning Objectives
    • Use biological databases to generate and compare lists of predicted miR targets, and obtain the mRNA sequence of their selected candidate gene
    • Use bioinformatics tools to design and optimize primer sets for qPCR
  • DNA barcoding research in first-year biology curriculum

    CURE-all: Large Scale Implementation of Authentic DNA Barcoding Research into First-Year Biology Curriculum

    Learning Objectives
    Students will be able to: Week 1-4: Fundamentals of Science and Biology
    • List the major processes involved in scientific discovery
    • List the different types of scientific studies and which types can establish causation
    • Design experiments with appropriate controls
    • Create and evaluate phylogenetic trees
    • Define taxonomy and phylogeny and explain their relationship to each other
    • Explain DNA sequence divergence and how it applies to evolutionary relationships and DNA barcoding
    Week 5-6: Ecology
    • Define and measure biodiversity and explain its importance
    • Catalog organisms using the morphospecies concept
    • Geographically map organisms using smartphones and an online mapping program
    • Calculate metrics of species diversity using spreadsheet software
    • Use spreadsheet software to quantify and graph biodiversity at forest edges vs. interiors
    • Write a formal lab report
    Week 7-11: Cellular and Molecular Biology
    • Extract, amplify, visualize and sequence DNA using standard molecular techniques (PCR, gel electrophoresis, Sanger sequencing)
    • Explain how DNA extraction, PCR, gel electrophoresis, and Sanger sequencing work at the molecular level
    Week 12-13: Bioinformatics
    • Trim and assemble raw DNA sequence data
    • Taxonomically identify DNA sequences isolated from unknown organisms using BLAST
    • Visualize sequence data relationships using sequence alignments and gene-based phylogenetic trees
    • Map and report data in a publicly available online database
    • Share data in a formal scientific poster
  • 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.
  • Genome view obtained from the integrated genome viewer: screenshot of Illumina 75bp single-end reads from two rockfishes Sebastes chrysomelas (top) and S. carnatus (bottom) aligned to a closely related reference genome (S. rubrivinctus).  Reads shown are within the coding region of a gene that was located in an island of genomic divergence between the two species.  The CT mutation within S. carnatus is predicted to cause an amino acid substitution from Lysine to Phenylalanine in a taste receptor gene.  This

    An Introduction to Eukaryotic Genome Analysis in Non-model Species for Undergraduates: A tutorial from the Genome...

    Learning Objectives
    At the end of the activity, students will be able to:
    • Explain the steps involved in genome assembly, annotation, and variant detection to other students and instructors.
    • Create meaningful visualizations of their data using the integrated genome viewer.
    • Use the Linux command line and web-based tools to answer research questions.
    • Produce annotated genomes and call variants from raw sequencing reads in non-model species.
  • SNP model by David Eccles (gringer) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 4.0 (http://creativecommons.org/licenses/by/4.0)], via Wikimedia Commons

    Exploration of the Human Genome by Investigation of Personalized SNPs

    Learning Objectives
    Students successfully completing this lesson will be able to:
    • Effectively use the bioinformatics databases (SNPedia, the UCSC Genome Browser, and NCBI) to explore SNPs of interest within the human genome.
    • Identify three health-related SNPs of personal interest and use the UCSC Genome Browser to define their precise chromosomal locations and determine whether they lie within a gene or are intergenic.
    • Establish a list of all genome-wide association studies correlated with a particular health-related SNP.
    • Predict which model organism would be most appropriate for conducting further research on a human disease.
  • 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
  • Peterson MP, Rosvall KA, Choi J-H, Ziegenfus C, Tang H, Colbourne JK, et al. (2013) Testosterone Affects Neural Gene Expression Differently in Male and Female Juncos: A Role for Hormones in Mediating Sexual Dimorphism and Conflict. PLoS ONE 8(4): e61784. doi:10.1371/journal.pone.0061784

    Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active Teaching

    Learning Objectives
    • From raw RNAseq data, run a basic analysis culminating in a list of differentially expressed genes.
    • Explain and evaluate statistical tests in RNAseq data. Specifically, given the output of a particular test, students should be able to interpret and explain the result.
    • Use the Linux command line to complete specified objectives in an RNAseq workflow.
    • Generate meaningful visualizations of results from new data in R.
    • (In addition, each chapter of this lesson plan contains more specific learning objectives, such as “Students will demonstrate their ability to map reads to a reference.”)
  • 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.
  • 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.