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| <center>'''BIOL47120 Biomedical Genomics II'''</center> | | <center>Bioinformatics Boot Camp for Ecology & Evolution: '''Pathogen Evolutionary Genomics'''</center> |
| <center>Spring 2019</center> | | <center>Thursday, Aug 6, 2020, 2 - 3:30pm</center> |
| <center>'''Instructor:''' Weigang Qiu, Ph.D.<br>Professor, Department of Biological Sciences, City University of New York, Hunter College & Graduate Center<br>Adjunct Faculty, Department of Physiology and Biophysics, | | <center>'''Instructors:''' Dr Weigang Qiu & Ms Saymon Akther</center> |
| Institute for Computational Biomedicine, Weil Cornell Medical College</center>
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| <center>'''Office:''' B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA</center>
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| <center>'''Email:''' weigang@genectr.hunter.cuny.edu</center> | | <center>'''Email:''' weigang@genectr.hunter.cuny.edu</center> |
| <center>'''Lab Website:''' http://diverge.hunter.cuny.edu/labwiki/</center> | | <center>'''Lab Website:''' http://diverge.hunter.cuny.edu/labwiki/</center> |
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| {| class="wikitable" | | {| class="wikitable" |
| |- | | |- |
| ! MA plot !! Volcano plot !! Heat map | | ! Lyme Disease (Borreliella) !! CoV Genome Tracker !! Coronavirus evolutuon |
| |- | | |- |
| | [[File:GeneExp1.jpeg|300px|thumbnail| fold change (y-axis) vs. total expression levels (x-axis)]] || | | | [[File:Lp54-gain-loss.png|300px|thumbnail| Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)]] || |
| [[File:GeneExp2.jpeg|300px|thumbnail| p-value (y-axis) vs. fold change (x-axis)]] | | [[File:Cov-screenshot-1.png|300px|thumbnail| [http://cov.genometracker.org/ Haplotype network] ]] |
| || | | || |
| [[File:GeneExp3.jpeg|300px|thumbnail| genes significantly down or up-regulated (at p<1e-4)]] | | [[File:Cov-screenshot-2.png|300px|thumbnail| Spike protein alignment ]] |
| |} | | |} |
| </center> | | </center> |
| ==Course Overview==
| | ---- |
| Welcome to Introductory BioMedical Genomics, a seminar course for advanced undergraduates and graduate students. A genome is the total genetic content of an organism. Driven by breakthroughs such as the decoding of the first human genome and rapid DNA and RNA-sequencing technologies, biomedical sciences are undergoing a rapid & irreversible transformation into a highly data-intensive field, that requires familiarity with concepts in both biology, computational, and data sciences.
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| Genome information is revolutionizing virtually all aspects of life sciences including basic research, medicine, and agriculture. Meanwhile, use of genomic data requires life scientists to be familiar with concepts and skills in biology, computer science, as well as data analysis.
| | ==Case studies from Qiu Lab== |
| | * [http://borreliabase.org Comparative genomics of worldwide Lyme disease pathogens] |
| | * [http://cov.genometracker.org Covid-19 Genome Tracker] |
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| This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises, using published studies.
| | ==CoV genome data set== |
| | * N=565 SARS-CoV-2 genomes collected during January & February 2020. Data source & acknowledgement [http://gisaid.org GIDAID] (<em>Warning: You need to acknowledge GISAID if you reuse the data in any publication</em>) |
| | * Download file: [http://diverge.hunter.cuny.edu/~weigang/qiu-akther.tar.gz data file] |
| | * Create a directory, unzip, & un-tar |
| | <syntaxhighlight lang='bash'> |
| | mkdir QiuAkther |
| | mv cov-camp.tar.gz QiuAkther/ |
| | cd QiuAkther |
| | tar -tzf cov-camp.tar.gz # view files |
| | tar -xzf cov-camp.tar.gz # un-zip & un-tar |
| | </syntaxhighlight> |
| | * View files |
| | <syntaxhighlight lang='bash'> |
| | file TCS.jar |
| | ls -lrt # long list, in reverse timeline |
| | less Jan-Feb.mafft # an alignment of 565 CoV2 genomes in FASTA format; "q" to quit |
| | less cov-565strains-617snvs.phy # non-gapped SNV alignment in PHYLIP format |
| | wc hap.txt # geographic origins |
| | head hap.txt |
| | wc group.txt # color assignment |
| | cat group.txt |
| | less cov-565strains.gml # graph file (output) |
| | </syntaxhighlight> |
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| The pre-requisites of the course are college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.
| | ==Bioinformatics Tools & Learning Goals== |
| | * BpWrapper: commandline tools for sequence, alignment, and tree manipulations (based on BioPerl). |
| | ** [https://github.com/bioperl/p5-bpwrapper Github Link] |
| | ** [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2074-9/figures/1 Flowchart from publication] |
| | * Haplotype network with TCS [https://pubmed.ncbi.nlm.nih.gov/11050560/ PubMed link] |
| | * Web-interactive visualization with [http://D3js.org D3js] |
| | ** [https://github.com/sairum/tcsBU Github link] |
| | ** [https://cibio.up.pt/software/tcsBU/index.html Web tool] |
| | ** [https://academic.oup.com/bioinformatics/article/32/4/627/1744448 Paper] |
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| ==Learning goals== | | ==Tutorial== |
| By the end of this course successful students will be able to:
| | * 2-2:30: Introduction on pathogen phylogenomics |
| * Describe next-generation sequencing (NGS) technologies & contrast it with traditional Sanger sequencing | | * 2:30-2:45: Demo: sequence manipulation with BpWrapper |
| * Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome.
| | <syntaxhighlight lang='bash'> |
| * Visualize and explore genomics data using RStudio
| | bioseq --man |
| * Replicate key results using a raw data set produced by a primary research paper
| | bioseq -n Jan-Feb.mafft |
| | | bioaln --man |
| ==Web Links==
| | bioaln -n -i'fasta' Jan-Feb.mafft |
| * Install R base: https://cloud.r-project.org
| | bioaln -l -i'fasta' Jan-Feb.mafft |
| * Install R Studio (Desktop version): http://www.rstudio.com/download
| | bioaln -n -i'phylip' cov-565strains-617snvs.phy |
| * Textbook: [http://r4all.org/#about Introduction to R for Biologists]
| | bioaln -l -i'phylip' cov-565strains-617snvs.phy |
| * Download: [http://www.r4all.org/books/datasets R datasets] | | FastTree -nt cov-565strains-617snvs.phy > cov.dnd |
| * A reference book: [https://r4ds.had.co.nz/ R for Data Science (Wickharm & Grolemund)]
| | biotree --man |
| | | biotree -n cov.dnd |
| ==Quizzes and Exams== | | biotree -l cov.dnd |
| Student performance will be evaluated by attendance, three (4) quizzes and a final report:
| | <syntaxhighlight> |
| * Attendance & In-class participation: 50 pts
| | * 2:45-3:00: build haplotype network with TCS |
| * Assignments: 5 x 10 = 50 pts
| | <syntaxhighlight lang='bash'> |
| * Quizzes: 2 x 25 pts = 50 pts
| | java -jar -Xmx1g TCS.jar |
| * Mid-term: 50 pts
| | <syntaxhighlight> |
| * Final presentation & report: 100 pts
| | * 3:00-3:15: interactive visualization with BuTCS |
| Total: 300 pts
| | * 3:15-3:30: Q & A |
| | |
| ==Tips for Success==
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| To maximize the your experience we strongly recommend the following strategies:
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| * Follow the directions for efficiently, finding high-impact papers, reading science research papers and preparing presentations.
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| * Read the papers, watch required videos and do the exercises regularly, long before you attend class.
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| * Attend all classes, as required. Late arrival results in loss of points.
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| * Keep up with online exercises. Don’t wait until the due date to start tasks.
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| * Take notes or annotate slides while attending the lectures.
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| * Listen actively and participate in class and in online discussions.
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| * Review and summarize material within 24 hrs after class.
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| * Observe the deadlines for submitting your work. Late submissions incur penalties.
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| * Put away cell phones, do not TM, email or play computer games in class.
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| ==Hunter/CUNY Policies==
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| * Policy on Academic Integrity
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| Hunter College regards acts of academic dishonesty (e.g., plagiarism, cheating on homework, online exercises or examinations, obtaining unfair advantage, and falsification of records and official documents) as serious offenses against the values of intellectual honesty. The College is committed to enforcing the CUNY Policy on Academic Integrity, and we will pursue cases of academic dishonesty according to the Hunter College Academic Integrity Procedures. Students will be asked to read this statement before exams.
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| * ADA Policy
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| In compliance with the American Disability Act of 1990 (ADA) and with Section 504 of the Rehabilitation Act of 1973, Hunter College is committed to ensuring educational parity and accommodations for all students with documented disabilities and/or medical conditions. It is recommended that all students with documented disabilities (Emotional, Medical, Physical, and/or Learning) consult the Office of AccessABILITY, located in Room E1214B, to secure necessary academic accommodations. For further information and assistance, please call: (212) 772- 4857 or (212) 650-3230.
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| * Syllabus Policy
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| Except for changes that substantially affect implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice, announced in class or posted on Blackboard.
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| ==Course Schedule==
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| ===Jan 26, 2019===
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| * Introduction
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| * Biblio Searching (NGS) & Group Assignments.
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| * R Tutorial: Chapter 1. user interface
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| * Assignment 1: Prepare Group Presentation; Upload by next Friday at 10pm
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| ===Feb 2, 2019===
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| * Group Presentations (20 min/20 slides) on Next-Generation Sequencing Technologies
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| ** Group 1 (Simran, Tiffany, Barbara). NGS vs Sanger
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| ** Group 2 (Kevin, Roland, Hamza): PacBio & Margos et al
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| ** Group 3 (Jerry, Kenneth, Muhammad): Illumina & Di et al
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| * R Tutorial, Chapter 2. How to get data into R
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| * Assignment 2: Using ggplot2 to visualize the "height" data set
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| ** boxplot: height by gender
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| ** density plot: density by gender
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| ** histogram by gender
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| ** Upload by next Friday at 10om
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| ===Feb 9, 2019===
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| * Group presentations
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| ** Group 4 (Jamie, Tony, Pavan: IonTorrent & NanoPore
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| ** Group 5 (Bing & Nevila) NGS applications in Lyme genomics
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| * R Tutorial. Chapter 3. Data manipulation wiht dplyr
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|
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| ===Feb 16, 2019===
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| * (Self study; No live class)
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| * Group assignments for research/application papers
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| ** Bac.genome: Tony & Jamie
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| ** Cancer.genome: Pavan & Kenetch
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| ** Chip-Seq: Simran & Jerry
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| ** Human genome: Roland & Barbara
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| ** Microbiome: Muhammad & Tiffany
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| ** RNA-Seq: Bing & Chris
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| ** Signle.Cell transcriptome: Kevin & Hamza
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| ** Each student should search the PubMed and identify one primary research paper for a 5-slide presentation next week
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| * R tutorial: Chapter 4. Data visualization with ggplot2
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| * Assignment #3: all R scripts and 5 plots in Chapter 4
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|
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| ===Feb 23, 2019===
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| (Belfer Building, Room BB401, East Conference Room)
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| * 5-slide presentation on selected paper, including Objective/Goal, Material & Methods, Main results (you want to replicate), & Available data sets and scripts
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| * Review for mid-term
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|
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| ===March 2, 2019===
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| * Mid-term exam, including
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| ** NGS terms & vocabulary
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| ** Advantages of NGS over traditional Sanger sequencing
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| ** R practicum: read data table into R; make tall tables; manipulation of data frame with dplyr; basic plots with ggplot2 | |
| ** Revise/Refine your last presentation by including the following parts:
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| *** Title slide: Paper citation, web links, group member names, date, version
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| *** Background/Objectives: 1 slide
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| *** Experimental samples & methods: 1-2 slides (including NGS tech used)
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| *** Analytical methods: software, main statistical methods (e.g., type of graphs, tests, and p-value interpretation)
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| *** Results: 1-2 graphs
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| *** Conclusion: 1 slide | |
| *** Supplemental Material: 1 data set you will be re-analyzing
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| *** Analytical plan: type of graphs & type of statistical tests
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| ===March 9, 2019===
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| * R tutorial: Section 5.2. Contingency analysis
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| * Group presentations (Data set identified) | |
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| ===March 16, 2019===
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| * R tutorial: Section 5.3. t-test
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| * Group presentations (Data visualization)
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| ===March 23, 2019===
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| * (Self study; No live class)
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| * Abstract (200 words; individualized; due 3/30)
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| * Review contingency test & two-sample t-test
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| * Generate preliminary graphs
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| ===March 30, 2019===
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| * 20 pts Quiz on contingency test & two-sample t-test
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| * Group presentations (Show preliminary graphs)
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| * Material & Methods (due 4/6)
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| ===April 6, 2019===
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| * 20 pts Quiz
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| * R tutorial: Section 5.4. Regression analysis
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| * Results (due 4/13)
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| ** Tables to show the dataset you work on (not all, but a sample)
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| ** Figures with legend (R methods, x & y-axis, conclusion)
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| ** 1-paragraph summary of your results
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| ===April 13, 2019===
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| * 20 pts Quiz. Regression analysis
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| * Background & Introduction (due 5/4)
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| ===May 4, 2019===
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| * Final presentation I. Graded on:
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| ** Objective (original & your own)
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| ** Material & methods (original & your own)
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| ** Results (your own)
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| ** Conclusion (your own)
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| ** Conclusion (due 5/11)
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| ===May 11, 2019===
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| * Self study: Prepare your 10-slide presentation
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| * No class (instructor travels)
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|
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| ===May 18, 2019, 9-1pm===
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| * Final presentation
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| * May 22, 2018 (Wed, 5pm) Final Report Due (ihard copy; n my office or in mailbox)
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