You are here
Search found 4 items
- (-) Remove Create graph, table etc. to present data filter Create graph, table etc. to present data
- (-) Remove Synthesis/Evaluation/Creation filter Synthesis/Evaluation/Creation
- (-) Remove Computer Model filter Computer Model
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.
Building Trees: Introducing evolutionary concepts by exploring Crassulaceae phylogeny and biogeographyLearning ObjectivesStudents 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.
Promoting Climate Change Literacy for Non-majors: Implementation of an atmospheric carbon dioxide modeling activity as...Learning Objectives
- Students will be able to manipulate and produce data and graphs.
- Students will be able to design a simple mathematical model of atmospheric CO2 that can be used to make predictions.
- Students will be able to conduct simulations, analyze, interpret, and draw conclusions about atmospheric CO2 levels from their own computer generated simulated data.
Investigating Cell Signaling with Gene Expression DatasetsLearning ObjectivesStudents 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.