Summer 2019 and Southwest-University: Difference between pages

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=Rules of Conduct=
<center>'''Biomedical Genomics'''</center>
# No eating, drinking, or loud talking in the lab. Socialize in the lobby only.
<center>July 8-19, 2019</center>
# Be respectful to each other, regardless of level of study
<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
# Be on time & responsible. Communicate in advance with the PI if late or absent
Institute for Computational Biomedicine, Weil Cornell Medical College</center>
# No use of phone or laptop during lab meetings
<center>'''Office:''' B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA</center>
=Schedule=
<center>'''Email:''' weigang@genectr.hunter.cuny.edu</center>
* June 19 (Wed). Summer research kickoff. Papers assigned. To prepare for Python tutorial, install the jupyter notebook in one of two following ways (by Edgar):
<center>'''Lab Website:''' http://diverge.hunter.cuny.edu/labwiki/</center>
** Installing the Anaconda Distribution (https://www.anaconda.com/distribution/#download-section): This is the easiest way to install Python on your machine. It also comes with a lot of packages for data science. However, it is quite heavy (~3GB), so if space is an issue you can try installing Miniconda. If you choose to install Anaconda, you don't need to install any additional packages since they are going to be installed automatically. Make sure you download Python 3.
----
** Installing Miniconda3 (https://docs.conda.io/en/latest/miniconda.html): This is like a mini version of Anaconda that comes with the Conda package manager and Python. It doesn't include any packages so it requires less space.
[[File:Lp54-gain-loss.png|200px|thumbnail|Figure 1. Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)]]
*** Installing on MacOs: https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html
==Course Overview==
*** Installing on Windows: https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html
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 profound transformation into a highly data-intensive field.  
*** Once you install Miniconda, you can use the conda command on your terminal to install other packages:
<syntaxhighlight lang='bash'>
conda install numpy
conda install pandas
conda install matplotlib
conda install jupyter
</syntaxhighlight>
* June 21 (Fri). Python Tutorial I. Jupyter notebook, string, list, conditions, loops (by Edgar)
* June 24 (Mon). Python Tutorial II. string functions, function, dictionary, modules (by Edgar)
* June 26 (Wed). Python Tutorial III. BioPython (Edgar & Muhammud)
* June 27 (Thur). Paper presentations


=Participants=
Genome information is revolutionizing virtually all aspects of life sciences including basic basic, 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.  
# Dr Oliver Attie, Research Associate
# Brian Sulkow, Research Associate
# Saymon Akther, CUNY Graduate Center, EEB Program
# Lily Li, CUNY Graduate Center, EEB Program
# Christopher Panlasigui, Hunter Biology
# Summer Interns: Muhammad, Radhika Mohan, Oscar Eng, Oliver Cai
=Journal Club=
# [http://diverge.hunter.cuny.edu/labwiki/First_Time_Guide#A_UNIX_.26_Perl_Primer a Unix & Perl tutorial]
# A short introduction to molecular phylogenetics: http://www.ncbi.nlm.nih.gov/pubmed/12801728
# A review on Borrelia genomics: https://www.ncbi.nlm.nih.gov/pubmed/24704760
# A model of immune selection: He et al (2018). https://www.nature.com/articles/s41467-018-04219-3
# A model of flu evolution: Neher et al (2016). https://www.pnas.org/content/113/12/E1701?ijkey=72c6025e999dd043d32f6822dc06c7356d8494b2&keytype2=tf_ipsecsha


=Projects=
This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises.
==Borrelia genome evolution (Saymon)==
 
# genome tree & phylogeography
The pre-requisites of the course includes college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.
# genome intragression
 
==Antigen evolution==
==Learning goals==
# Borrelia OspC
By the end of this course successful students will be able to:
# Immune selection model
* Describe next-generation sequencing  (NGS) technologies & contrast it with traditional Sanger sequencing
# Flue
* Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome, and single-cell genomics
* Visualize and explore genomics data using RStudio
* Replicate key results using a data set associated with a primary research paper
 
==Useful links==
* Install R and R Studio
* Unix Tutorial
* Textbook
 
==Quizzes and Exams==
Student performance will be evaluated by attendance, three (4) quizzes and a final report:
* Attendance: 50 pts
* Quizzes: 4 x 25 pts = 100 pts
* Final report: 50 pts
Total: 200 pts
 
==Course Schedule==
* July 8 (Mon), 8:40-12:10
* July 9 (Tu), 8:40-12:10
* July 10 (Wed), 8:40-12:10
* July 11 (Thur), 8:40-12:10
* July 12 (Fri), 8:40-12:10
* July 15 (Mon), 8:00-12:10
* July 16 (Tu), 8:00-12:10
* July 17 (Wed), 8:00-12:10
* July 18 (Thur), 8:00-12:10
* July 19 (Fri), 8:00-12:10
 
==Papers & Data==
{| class="wikitable sortable"
|-
! Omics Application !! Paper link !! Data set !! NGS Technology
|-
| Microbiome || [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193652 Rimoldi_etal_2018_PlosOne] || [https://doi.org/10.1371/journal.pone.0193652.s004 S1 Dataset] || 16S rDNA amplicon sequencing
|-
| Transcriptome || [https://science.sciencemag.org/content/350/6264/1096 Wang_etal_2015_Science] || Tables S2 & S4 || RNA-Seq
|-
| Transcriptome & Regulome || [https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-019-0477-8 Nava_etal_2019_BMCGenomics] || Tables S2 & S3 || RNA-Seq/CHIP-Seq
|-
| Example || Example || Example || Example
|-
| Example || Example || Example || Example
|-
| Example || Example || Example || Example
|-
| Example || Example || Example || Example
|-
| Example || Example || Example || Example
|-
| Example || Example || Example || Example
|-
| Example || Example || Example || Example
|}

Revision as of 22:07, 21 June 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/

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 profound transformation into a highly data-intensive field.

Genome information is revolutionizing virtually all aspects of life sciences including basic basic, 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.

The pre-requisites of the course includes 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, and single-cell genomics
  • Visualize and explore genomics data using RStudio
  • Replicate key results using a data set associated with a primary research paper

Useful links

  • Install R and R Studio
  • Unix Tutorial
  • Textbook

Quizzes and Exams

Student performance will be evaluated by attendance, three (4) quizzes and a final report:

  • Attendance: 50 pts
  • Quizzes: 4 x 25 pts = 100 pts
  • Final report: 50 pts

Total: 200 pts

Course Schedule

  • July 8 (Mon), 8:40-12:10
  • July 9 (Tu), 8:40-12:10
  • July 10 (Wed), 8:40-12:10
  • July 11 (Thur), 8:40-12:10
  • July 12 (Fri), 8:40-12:10
  • July 15 (Mon), 8:00-12:10
  • July 16 (Tu), 8:00-12:10
  • July 17 (Wed), 8:00-12:10
  • July 18 (Thur), 8:00-12:10
  • July 19 (Fri), 8:00-12:10

Papers & Data

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
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example