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Biochemistry And Molecular Biology
Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence...Learning ObjectivesAt 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.
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.
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.
An undergraduate bioinformatics curriculum that teaches eukaryotic gene structureLearning ObjectivesModule 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.
- 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.
- 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:
- 5' capping
- 3' polyadenylation
- 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.
- 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.
- 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.
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.