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Exploration of the Human Genome by Investigation of Personalized SNPsLearning ObjectivesStudents 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.
Tackling "Big Data" with Biology Undergrads: A Simple RNA-seq Data Analysis Tutorial Using GalaxyLearning 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.
Predicting and classifying effects of insertion and deletion mutations on protein coding regionsLearning ObjectivesStudents 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
Using QIIME to Interpret Environmental Microbial Communities in an Upper Level Metagenomics CourseLearning ObjectivesStudents 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.