Southwest-University: Difference between revisions

From QiuLab
Jump to navigation Jump to search
imported>Weigang
imported>Weigang
(16 intermediate revisions by the same user not shown)
Line 1: Line 1:
<center>'''Biomedical Genomics'''</center>
<center>'''Biomedical Genomics'''</center>
<center>July 8-19, 2019</center>
<center>July 8-19, 2019</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>'''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,
Institute for Computational Biomedicine, Weil Cornell Medical College</center>
Institute for Computational Biomedicine, Weil Cornell Medical College</center>
<center>'''Office:''' B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA</center>
<center>'''Office:''' B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA</center>
<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>
<br>
<center>'''Host''': Shunqin Zhu (祝顺琴), Ph.D.<br>Associate Professor, School of  Life Science, South West University</center>
----
----
[[File:Lp54-gain-loss.png|400px|thumbnail|Figure 1. Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)]]
[[File:Lp54-gain-loss.png|300px|thumbnail|Figure 1. Gains & losses of host-defense genes among Lyme pathogen genomes ([https://www.ncbi.nlm.nih.gov/pubmed/24704760 Qiu & Martin 2014])]]
==Course Overview==
==Course Overview==
Welcome to BioMedical Genomics, a computer workshop 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 next-generation DNA -sequencing technologies, biomedical sciences are undergoing a rapid and irreversible transformation into a highly data-intensive field.  
Welcome to BioMedical Genomics, a computer workshop 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 next-generation DNA -sequencing technologies, biomedical sciences are undergoing a rapid and irreversible transformation into a highly data-intensive field.  
Line 32: Line 34:
==Quizzes and Exams==
==Quizzes and Exams==
Student performance will be evaluated by attendance, three (4) quizzes and a final report:
Student performance will be evaluated by attendance, three (4) quizzes and a final report:
* Attendance: 50 pts  
* Attendance: 50 pts
* Assignments: 5 x 10 = 50 pts  
* Quizzes: 2 x 25 pts = 50 pts
* Quizzes: 2 x 25 pts = 50 pts
* Mid-term: 50 pts
* Mid-term: 50 pts
* Final presentation: 50 pts
* Final presentation: 50 pts
Total: 200 pts
Total: 250 pts


==Course Schedule==
==Course Schedule==
Line 43: Line 46:
! Date & Hour !! Tutorials !! Assignment !! Quiz & Exam
! Date & Hour !! Tutorials !! Assignment !! Quiz & Exam
|-
|-
| July 8 (Mon), 8:40-12:10 || Introduction; R Tutorial I; NGS ||  
| July 8 (Mon), 8:40-12:10 || Introduction; R Tutorial I;
Assignment #1
[[Lecture slides:R-part-1-small.pdf|thumbnail|Lecture slides]]
* List pros & cons of Sanger vs NGS
||  
* Compare accuracy, read length, and error rate between Illumina and PacBio
Assignment #1 (create a WORD document including scripts & graphs (i.e., compile your work into a lab report, due tomorrow)
* Install R/R studio and the "tidyverse" package on your own computer
* Install R/R studio and the "tidyverse" package on your own computer
* Recreate Script 1 & Mini-Practical
* Recreate Script 1 & Mini-Practical
* Show help page for function "seq"
* Show help page for function "seq"
* Download dataset
** Create a new folder (e.g., Desktop/rtutor)
** Create a sub-folder (e.g., Desktop/rtutor/data/)
** Download from http://www.r4all.org/the-book/datasets
** Save to the sub-folder
** Unzip the file
   ||  
   ||  
|-
|-
| July 9 (Tu), 8:40-12:10 || NGS; R Tutorial II ||  
| July 9 (Tu), 8:40-12:10 || NGS; R Tutorial II ||  
Assignment #2
Assignment #2
* List pros & cons of Sanger vs NGS
* Compare accuracy, read length, and error rate between Illumina and PacBio
* Describe sequence information captured with each of the following file formats: FASTA, FASTQ, SAM, VCF  
* Describe sequence information captured with each of the following file formats: FASTA, FASTQ, SAM, VCF  
* Wide vs Tall data frames
* Wide vs Tall data frames
Line 77: Line 89:
|-
|-
| July 16 (Tu), 8:00-12:10 || Proteome ||  
| July 16 (Tu), 8:00-12:10 || Proteome ||  
Assignment #6
  ||  
  ||  
|-
|-
| July 17 (Wed), 8:00-12:10 || Genomics I ||  
| July 17 (Wed), 8:00-12:10 || Genomics I ||  
Assignment #7
  || Quiz II
  || Quiz II
|-
|-

Revision as of 06:09, 8 July 2019

Biomedical Genomics
July 8-19, 2019
Instructor: Weigang Qiu, Ph.D.
Professor, Department of Biological Sciences, City University of New York, Hunter College & Graduate Center
Adjunct Faculty, Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weil Cornell Medical College
Office: B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
Email: weigang@genectr.hunter.cuny.edu
Lab Website: http://diverge.hunter.cuny.edu/labwiki/


Host: Shunqin Zhu (祝顺琴), Ph.D.
Associate Professor, School of Life Science, South West University

Figure 1. Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)

Course Overview

Welcome to BioMedical Genomics, a computer workshop 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 next-generation DNA -sequencing technologies, biomedical sciences are undergoing a rapid and irreversible transformation into a highly data-intensive field.

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.

This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises, using 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 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, three (4) quizzes and a final report:

  • Attendance: 50 pts
  • Assignments: 5 x 10 = 50 pts
  • Quizzes: 2 x 25 pts = 50 pts
  • Mid-term: 50 pts
  • Final presentation: 50 pts

Total: 250 pts

Course Schedule

Date & Hour Tutorials Assignment Quiz & Exam
July 8 (Mon), 8:40-12:10 Introduction; R Tutorial I;

thumbnail|Lecture slides

Assignment #1 (create a WORD document including scripts & graphs (i.e., compile your work into a lab report, due tomorrow)

  • Install R/R studio and the "tidyverse" package on your own computer
  • Recreate Script 1 & Mini-Practical
  • Show help page for function "seq"
  • Download dataset
July 9 (Tu), 8:40-12:10 NGS; R Tutorial II

Assignment #2

  • List pros & cons of Sanger vs NGS
  • Compare accuracy, read length, and error rate between Illumina and PacBio
  • Describe sequence information captured with each of the following file formats: FASTA, FASTQ, SAM, VCF
  • Wide vs Tall data frames
  • Variable names (informative, case sensitive)
  • Read file
July 10 (Wed), 8:40-12:10 Microbiome I; R Tutorial III

Assignment #3

Quiz I
July 11 (Thur), 8:40-12:10 Microbiome II; R Tutorial IV

Assignment #4

July 12 (Fri), 8:40-12:10 Mid-term Exam
Weekend Break
July 15 (Mon), 8:00-12:10 Transcriptome; R Tutorial V

Assignment #5

July 16 (Tu), 8:00-12:10 Proteome
July 17 (Wed), 8:00-12:10 Genomics I Quiz II
July 18 (Thur), 8:00-12:10 Genomics II
July 19 (Fri), 8:00-12:10 Presentations

Papers & Datasets

Omics Application Paper link Data set NGS Technology
Microbiome Rimoldi_etal_2018_PlosOne S1 Dataset 16S rDNA amplicon sequencing
Transcriptome Wang_etal_2015_Science Tables S2 & S4 RNA-Seq
Transcriptome & Regulome Nava_etal_2019_BMCGenomics Tables S2 & S3 RNA-Seq & CHIP-Seq
Proteome Qiu_etal_2017_NPJ (to be posted) SILAC
Population genomics (Lyme) Di_etal_2018_JCM Data & R codes Amplicon sequencing (antigen locus)
Population genomics/GWAS (Human) Simonti_etal_2016_Science Table S2 whole-genome sequencing (WGS); 1000 Genome Project (IGSR)
TB surveillance Brow_etal_2015 Sequence Archives Whole-genome sequencing (WGS)
Example Example Example Example
Example Example Example Example
Example Example Example Example