Tutorials

BCRF tutorials offer members of the Department of Biochemistry an opportunity to learn new computer programs that can improve their research.

Workshops feature PyMOL, Python, R and RStudio, the department’s Linux Cluster, and more.

The classes are held (unless otherwise specified) in Room 201 of the Hector F. DeLuca Biochemistry Laboratories (433 Babcock) with six iMacs and a large LED screen augmented with two smaller screens to better see presentations.

Jean-Yves Sgro, a senior scientist with years of experience in using and teaching computer programs, teaches the workshops. Class size is kept small for better interaction and hands-on opportunities but occasionally can be augmented with two or three attendees bringing their laptops.

Workshops typically last two hours. All tutorial materials are available for individuals to use after courses or on their own (see at the bottom of this page). Classroom size is kept small for better interaction, BCRF tutorials are typically two hours and involve a lot of hands-on practice.

 

 

Classroom location (unless otherwise specified) is in Room 201 of the Hector F. DeLuca Biochemistry Laboratories.

Photo of 201 Biochemistry Laboratory "mini workshop" classroom

 


Tutorial Software

The tutorial exercises below are for the most part written in the spirit of "learning by doing" and could also be used by students to learn on their own. The tutorial exercises assume no prior knowledge or experience and start from the very basic.

The software used for the tutorials is either available to Biochemistry personnel, UW-Madison personnel, or is "open source." The current list of software is as follows:

MOLECULAR GRAPHICS:

STRUCTURAL MODELING:

DNASTAR/LASERGENE:

  • Lasergene suite(s). Official site: dnastar.com. Biochemistry personnel can download from Intranet

R STATISTICAL SOFTWARE: R, RStudio, Bioconductor

  • R: This multiplatform (Windows, Mac, Linux) open source software can be downloaded from The Comprehensive R Archive Network. UW-Madison personnel can download a version (Windows/Mac only) from Campus Software Library 
  • RStudio: We use an open-source R interface RStudio to explore “reproducible research”
  • Bioconductor is a series of R packages designed for the analysis of many types of biological data and has a parallel development to R. Download and installation: bioconductor.org

    UNIX/LINUX:

    • Learning the basic operations to navigate a line-command Terminal in Unix/Linux or even MacOS is useful or even necessary to master in anticipation of other activities such as using a Linux cluster.

    PYTHON 3:

    • "Learn by doing" and 

      start with no prior knowledge as a complete beginner.


    Tutorial Exercises

    Note: Not all formats (HTML, PDF etc.) are available for all workshops; some extra formats may be available when filling-in workshop evaluations.

    MOLECULAR GRAPHICSPyMOL , Chimera 

    STRUCTURAL MODELING: MODELLER, Autodock, Autodock Vina

    DNASTAR/LASERGENE:  DNAStar/ArrayStar: analysis of public datasets |  DNAStar: Templated RNA-Seq.

    R STATISTICAL SOFTWAREIntroduction to RR / RStudio: creating reports for “reproducible research”
    R / Bioconductor: from raw data to annotated gene listR / Bioconductor: analysis of a vitaminD public dataset

    UNIX/LINUXBasic Unix | Accessing SBGrid | Accessing Linux cluster and HTcondor 

    PYTHON:   Series I,II, III, IV - Analyze and plot from Tabular data. Beginner level.


    PyMOL

     
    Note: A 173 page workbook is provided (loaned) for use during classes.

    Part I

    Part II


    Chimera (last updated: March 28, 2017)

    • UCSF Chimera I: molecular graphics: HTML, PDF, DOCX

    MODELLER (last updated: April 4, 2017 | April 11, 2017)


    Autodock on linux cluster (last updated: April 18, 2017)

    • Running Autodock & Autodock Vina on BCC Linux cluster with HTCondor - PDBQT files: protein | ligand
    • HTML, PDF, DOCX

     DNAStar/ArrayStar: analysis of public datasets (last update: 2014)

     Example with microarray data GSE27973


     DNAStar: Templated RNA-Seq (last updated: February 21, 2017)

     Example based on DNASTAR Tutorial Libray for Next-Gen Sequencing (Tutorial 3: Templated RNA-Seq Workflow)


    Introduction to R (last updated: April 25, 2017)

    In class short hand out (rendered ipython jupyter notebook)


    R / RStudio: creating reports for “reproducible research” (last updated: May 2, 2017)

    In class short hand out  (rendered ipython jupyter notebook) 

    External link resource(s):


    R / Bioconductor: from raw data to annotated gene list (last updated: May 9, 2017)

    In class short hand out (rendered ipython jupyter notebook) 

    • Bioconductor: installation instructions: HTML, PDF
    • From CEL Files to Annotated Gene Lists: HTML, PDF (based on chapter 25 of book cited below)

    Note: Tutorial is based on the last chapter of the book Bioinformatics and Computational Biology Solutions Using R and Bioconductor


    R / Bioconductor: analysis of a vitaminD public dataset (last updated: May 9, 2017)

    In class short hand out (rendered ipython jupyter notebook) 


      Basic Unix (last updated: December 7, 2016)

      In class short hand out (rendered ipython jupyter notebook)  - PDF

      Extra hand outs:

      • Dos to Unix commands (HTML, PDF). 
      • Unix/Linux Command Reference (PDF)

      Accessing SBGrid (Restricted - last updated: 2016)


      Accessing Linux cluster and HTCondor (Restricted - last updated: 2016)

      In class short hand out (rendered ipython jupyter notebook)  - PDF

       


      Python Data Analysis (last updated: February 16-28, 2017)

      • Python Data Analysis I - (HTML, PDF, DOCX) - short Hand-out (rendered ipython jupyter notebook)
      • Python Data Analysis II - (HTML, PDF, DOCX) - short Hand-out (rendered ipython jupyter notebook)
      • Python Data Analysis III - (HTML, PDF, DOCX) - short Hand-out (rendered ipython jupyter notebook)
      • Python Data Analysis IV - (HTML, PDF, DOCX) - short Hand-out (rendered ipython jupyter notebook)
      • Python Data Analysis IV_2 - short Hand-out (rendered ipython jupyter notebook)