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- (-) Remove Bioinformatics filter Bioinformatics
- (-) Remove Ability to use modeling and simulation filter Ability to use modeling and simulation
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.
Using computational molecular modeling software to demonstrate how DNA mutations cause phenotypesLearning ObjectivesStudents successfully completing this lesson will:
- Practice basic molecular biology laboratory skills such as DNA isolation, PCR, and gel electrophoresis.
- Gather and analyze quantitative and qualitative scientific data and present it in figures.
- Use bioinformatics to analyze DNA sequences and obtain protein sequences for molecular modeling.
- Make and analyze three-dimensional (3-D) protein models using molecular modeling software.
- Write a laboratory report using the collected data to explain how mutations in the DNA cause changes in protein structure/function which lead to mutant phenotypes.
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.
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.
Homologous chromosomes? Exploring human sex chromosomes, sex determination and sex reversal using bioinformatics...Learning ObjectivesStudents 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
A CURE-based approach to teaching genomics using mitochondrial genomesLearning Objectives
- Install the appropriate programs such as Putty and WinSCP.
- Navigate NCBI's website including their different BLAST programs (e.g., blastn, tblastx, blastp and blastx)
- Use command-line BLAST to identify mitochondrial contigs within a whole genome assembly
- Filter the desired sequence (using grep) and move the assembled mitochondrial genome onto your own computer (using FTP or SCP)
- Error-correct contigs (bwa mem, samtools tview), connect and circularize organellar contigs (extending from filtered reads)
- Transform assembled sequences into annotated genomes
- Orient to canonical start locations in the mitochondrial genome (cox1)
- Identify the boundaries of all coding components of the mitochondrial genome using BLAST, including: Protein coding genes (BLASTx and tBLASTX), tRNAs (proprietary programs such as tRNAscan), rRNAs (BLASTn, Chlorobox), ORFs (NCBI's ORFFinder)
- Deposit annotation onto genome repository (NCBI)
- Update CV/resume to reflect bioinformatics skills learned in this lesson
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.