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==Course Schedule==
==Course Schedule==
===Jan 26, 2019===
===Feb 1, 2020===
* Introduction
* Introduction
* Biblio Searching (NGS) & Group Assignments.  
* Biblio Searching (NGS) & Group Assignments.  
Line 75: Line 75:
* Assignment 1: Prepare Group Presentation; Upload by next Friday at 10pm
* Assignment 1: Prepare Group Presentation; Upload by next Friday at 10pm


===Feb 2, 2019===
===Feb 8, 2019===
* Group Presentations (20 min/20 slides) on Next-Generation Sequencing Technologies
* Group Presentations (20 min/20 slides) on Next-Generation Sequencing Technologies
** Group 1 (Simran, Tiffany, Barbara). NGS vs Sanger
** Group 2 (Kevin, Roland, Hamza): PacBio & Margos et al
** Group 3 (Jerry, Kenneth, Muhammad): Illumina & Di et al
* R Tutorial, Chapter 2. How to get data into R
* R Tutorial, Chapter 2. How to get data into R
* Assignment 2: Using ggplot2 to visualize the "height" data set
* Assignment 2: Using ggplot2 to visualize the "height" data set
** boxplot: height by gender
** density plot: density by gender
** histogram by gender
** Upload by next Friday at 10om


===Feb 9, 2019===
===Feb 15, 2019===
* Group presentations
* Group presentations
** Group 4 (Jamie, Tony, Pavan: IonTorrent & NanoPore
** Group 5 (Bing & Nevila) NGS applications in Lyme genomics
* R Tutorial. Chapter 3. Data manipulation wiht dplyr
* R Tutorial. Chapter 3. Data manipulation wiht dplyr
* Assignment 3: all R scripts and 5 plots in Chapter 4
===Feb 16, 2019===
 
* (Self study; No live class)
===Feb 22, 2019===
* Group assignments for research/application papers
* Group assignments for research/application papers: Each student should search the PubMed and identify one primary research paper for a 5-slide presentation next week
** Bac.genome: Tony & Jamie
** Bacterial genomics
** Cancer.genome: Pavan & Kenetch
** Metagenomics
** Chip-Seq: Simran & Jerry
** Microbiome
** Human genome: Roland & Barbara
** Cancer genomics
** Microbiome: Muhammad & Tiffany
** Chip-Seq
** RNA-Seq: Bing & Chris
** Human genome
** Signle.Cell transcriptome: Kevin & Hamza
** RNA-Seq
** Each student should search the PubMed and identify one primary research paper for a 5-slide presentation next week
** Signle Cell transcriptome
* R tutorial: Chapter 4. Data visualization with ggplot2
* R tutorial: Chapter 4. Data visualization with ggplot2
* Assignment #3: all R scripts and 5 plots in Chapter 4
* Assignment #4
 
===Feb 23, 2019===
===Feb 29, 2019===
(Belfer Building, Room BB401, East Conference Room)
* 5-slide presentation on selected paper, including Objective/Goal, Material & Methods, Main results (you want to replicate), & Available data sets and scripts
* 5-slide presentation on selected paper, including Objective/Goal, Material & Methods, Main results (you want to replicate), & Available data sets and scripts
* Review for mid-term
* Review for mid-term
   
   
===March 2, 2019===
===March 7, 2019===
* Mid-term exam, including
* Mid-term exam, including
** NGS terms & vocabulary
** NGS terms & vocabulary
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*** Analytical plan: type of graphs & type of statistical tests
*** Analytical plan: type of graphs & type of statistical tests


===March 9, 2019===
===March 14, 2019===
* R tutorial: Section 5.2. Contingency analysis
* R tutorial: Section 5.2. Contingency analysis
* Group presentations (Data set identified)
* Group presentations (Data set identified)
   
   
===March 16, 2019===
===March 22, 2019===
* R tutorial: Section 5.3. t-test
* R tutorial: Section 5.3. t-test
* Group presentations (Data visualization)
* Group presentations (Data visualization)
   
   
===March 23, 2019===
===March 28, 2019===
* (Self study; No live class)
* (Self study; No live class)
* Abstract (200 words; individualized; due 3/30)
* Abstract (200 words; individualized; due 3/30)
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* Material & Methods (due 4/6)
* Material & Methods (due 4/6)


===April 6, 2019===  
===April 4, 2019===  
* 20 pts Quiz
* 20 pts Quiz
* R tutorial: Section 5.4. Regression analysis
* R tutorial: Section 5.4. Regression analysis
Line 154: Line 144:
** 1-paragraph summary of your results
** 1-paragraph summary of your results


===April 13, 2019===
===April 18, 2019===
* 20 pts Quiz. Regression analysis
* 20 pts Quiz. Regression analysis
* Background & Introduction (due 5/4)
* Background & Introduction (due 5/4)


===May 4, 2019===
===April 25, 2019===
* Final presentation I. Graded on:
* Final presentation I. Graded on:
** Objective (original & your own)
** Objective (original & your own)
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** Conclusion (due 5/11)
** Conclusion (due 5/11)


===May 11, 2019===
===May 2, 2019===
* Self study: Prepare your 10-slide presentation
* Self study: Prepare your 10-slide presentation
* No class (instructor travels)
* No class (instructor travels)
   
   
===May 18, 2019, 9-1pm===
===May 16, 2019, 9-1pm===
* Final presentation
* Final presentation
* May 22, 2018 (Wed, 5pm) Final Report Due (ihard copy; n my office or in mailbox)
* May 22, 2018 (Wed, 5pm) Final Report Due (hard copy; n my office or in mailbox)

Revision as of 15:25, 29 January 2020

BIOL47120 Biomedical Genomics II
Spring 2020, Saturdays 9-12 noon, Hunter North Building 1001G
Instructor: Weigang Qiu, Ph.D.
Professor, Department of Biological Sciences, City University of New York, Hunter College & Graduate Center
Office: B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
Email: weigang@genectr.hunter.cuny.edu
MA plot Volcano plot Heat map
fold change (y-axis) vs. total expression levels (x-axis)
p-value (y-axis) vs. fold change (x-axis)
genes significantly down or up-regulated (at p<1e-4)

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.

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 statistics.

This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises. Students are expected to be able to replicate key results of data analysis from published studies.

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.

Learning goals

By the end of this course successful students will be able to:

  • Describe next-generation sequencing (NGS) technologies & contrast it with traditional Sanger sequencing
  • Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome.
  • Visualize and explore genomics data using R & RStudio
  • Replicate key results using a raw data set produced by a primary research paper

Web Links

Quizzes and Exams

Student performance will be evaluated by attendance, weekly assignments, quizzes, and a final report:

  • Attendance & In-class participation: 50 pts
  • Assignments: 5 x 10 = 50 pts
  • Quizzes: 2 x 25 pts = 50 pts
  • Mid-term: 50 pts
  • Final presentation & report: 100 pts

Total: 300 pts

Tips for Success

To maximize the your experience we strongly recommend the following strategies:

  • Follow the directions for efficiently, finding high-impact papers, reading science research papers and preparing presentations.
  • Read the papers, watch required videos and do the exercises regularly, long before you attend class.
  • Attend all classes, as required. Late arrival results in loss of points.
  • Keep up with online exercises. Don’t wait until the due date to start tasks.
  • Take notes or annotate slides while attending the lectures.
  • Listen actively and participate in class and in online discussions.
  • Review and summarize material within 24 hrs after class.
  • Observe the deadlines for submitting your work. Late submissions incur penalties.
  • Put away cell phones, do not TM, email or play computer games in class.

Hunter/CUNY Policies

  • Policy on Academic Integrity

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.

  • ADA Policy

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.

  • Syllabus Policy

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.

Course Schedule

Feb 1, 2020

  • Introduction
  • Biblio Searching (NGS) & Group Assignments.
  • R Tutorial: Chapter 1. user interface
  • Assignment 1: Prepare Group Presentation; Upload by next Friday at 10pm

Feb 8, 2019

  • Group Presentations (20 min/20 slides) on Next-Generation Sequencing Technologies
  • R Tutorial, Chapter 2. How to get data into R
  • Assignment 2: Using ggplot2 to visualize the "height" data set

Feb 15, 2019

  • Group presentations
  • R Tutorial. Chapter 3. Data manipulation wiht dplyr
  • Assignment 3: all R scripts and 5 plots in Chapter 4

Feb 22, 2019

  • Group assignments for research/application papers: Each student should search the PubMed and identify one primary research paper for a 5-slide presentation next week
    • Bacterial genomics
    • Metagenomics
    • Microbiome
    • Cancer genomics
    • Chip-Seq
    • Human genome
    • RNA-Seq
    • Signle Cell transcriptome
  • R tutorial: Chapter 4. Data visualization with ggplot2
  • Assignment #4

Feb 29, 2019

  • 5-slide presentation on selected paper, including Objective/Goal, Material & Methods, Main results (you want to replicate), & Available data sets and scripts
  • Review for mid-term

March 7, 2019

  • Mid-term exam, including
    • NGS terms & vocabulary
    • Advantages of NGS over traditional Sanger sequencing
    • 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:
      • Title slide: Paper citation, web links, group member names, date, version
      • Background/Objectives: 1 slide
      • Experimental samples & methods: 1-2 slides (including NGS tech used)
      • Analytical methods: software, main statistical methods (e.g., type of graphs, tests, and p-value interpretation)
      • Results: 1-2 graphs
      • Conclusion: 1 slide
      • Supplemental Material: 1 data set you will be re-analyzing
      • Analytical plan: type of graphs & type of statistical tests

March 14, 2019

  • R tutorial: Section 5.2. Contingency analysis
  • Group presentations (Data set identified)

March 22, 2019

  • R tutorial: Section 5.3. t-test
  • Group presentations (Data visualization)

March 28, 2019

  • (Self study; No live class)
  • Abstract (200 words; individualized; due 3/30)
  • Review contingency test & two-sample t-test
  • Generate preliminary graphs

March 30, 2019

  • 20 pts Quiz on contingency test & two-sample t-test
  • Group presentations (Show preliminary graphs)
  • Material & Methods (due 4/6)

April 4, 2019

  • 20 pts Quiz
  • R tutorial: Section 5.4. Regression analysis
  • Results (due 4/13)
    • Tables to show the dataset you work on (not all, but a sample)
    • Figures with legend (R methods, x & y-axis, conclusion)
    • 1-paragraph summary of your results

April 18, 2019

  • 20 pts Quiz. Regression analysis
  • Background & Introduction (due 5/4)

April 25, 2019

  • Final presentation I. Graded on:
    • Objective (original & your own)
    • Material & methods (original & your own)
    • Results (your own)
    • Conclusion (your own)
    • Conclusion (due 5/11)

May 2, 2019

  • Self study: Prepare your 10-slide presentation
  • No class (instructor travels)

May 16, 2019, 9-1pm

  • Final presentation
  • May 22, 2018 (Wed, 5pm) Final Report Due (hard copy; n my office or in mailbox)