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Bioinformatics

  • 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.
  • “The outcome of the Central Dogma is not always intuitive” Variation in gene size does not necessarily correlate with variation in protein size. Here, two related genes differ in length due to a deletion mutation that removes four nucleotides. Many students do not predict that the smaller gene, after transcription and translation, would produce a larger protein.

    Predicting and classifying effects of insertion and deletion mutations on protein coding regions

    Learning Objectives
    Students will be able to:
    • accurately predict effects of frameshift mutations in protein coding regions
    • conduct statistical analysis to compare expected and observed values
    • become familiar with accessing and using DNA sequence databases and analysis tools
  • Students use plastic Easter eggs and chocolate pieces to simulate the distribution of HIV in T lymphocytes.

    Infectious Chocolate Joy with a Side of Poissonian Statistics: An activity connecting life science students with subtle...

    Learning Objectives
    • Students will define a Poisson distribution.
    • Students will generate a data set on the probability of a T cell being infected with a virus(es).
    • Students will predict the likelihood of one observing the mean value of viruses occurring.
    • Students will evaluate the outcomes of a random process.
    • Students will hypothesize whether a process is Poissonian and design a test for that hypothesis.
    • Students will collect data and create a histogram from their data.
  • 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
  • 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.
  • 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.
  • 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
  • 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.
  • 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.”)
  • 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.
  • 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