Introduction to Highthroughput Data Analysis

class: center, middle

# Intro to GEN220

Jason Stajich
      
      http://hyphaltip.github.io/GEN220_2015/
      ---
      # GEN 220

* This course will cover basics of using UNIX and Python to do data analayses

* Focus is on genomics and sequence data but will also apply to any datasets where data can be accessed in text files and in particular column delimited data

* No prior programming experience required.

* You must have access to a computer that allows you to either run the terminal (e.g OSX or Linux) or run programs like SSH to log into UCR Cluster

* Some amount biology background will be assumed here. When in doubt ask questions. Others will be likely to have same questions!
      
      ---
      # Course Logistics
      1. Meeting in University Laboratory Building (ULB) Bioinformatics suite

2. Wednesdays scheduled for 2 hrs, Fridays for 1hr. Can extend
      this to 2 hrs on Friday with workshops if helpful.

3. Expect to have a mix of in-class workshops and
      lectures.

3. Homework problem sets will be assigned to give you
      more practice and improve skills

3. Laptops are available during class setting, but you will need
      to have access to a computer outside of class to complete assignments

4. Accounts to the IIGB Biocluster will be provided during the
      course. Most work will not require advanced tools on the cluster however.
      
      ---

# Resources

1. _Bioinformatics Data Skills: Reproducible and Robust Research
      with Open Source Tools_. Vince Buffalo. 2015 O'Reilly &
      Associates. [O'Reilly](http://shop.oreilly.com/product/0636920030157.do)
      [Amazon](http://smile.amazon.com/Bioinformatics-Data-Skills-Reproducible-Research/dp/1449367372)

2. _Unix and Perl to the Rescue: A Primer_. Keith Bradnam and Ian
      Korf. [Web PDF](http://korflab.ucdavis.edu/unix_and_Perl/) (free)

3. _Unix and Perl to the rescue!_ Bradnam and
      Korf. [website](http://rescuedbycode.com/).

4. [Rosalind](http://rosalind.info/problems/locations/) - An online platform to learn bioinformatics and programming in Python.

5. [Software Carpentry](http://swcarpentry.org)

---
      # Grading
      1. Homeworks (5 in total) will be worth 50% of the grade
      
      2. Team project - presentation, written report and code repository worth 50% of grade.
      
      3. You are expected to attend class - this is graduate school -
      so I don't take attendance. If you need be absent do let me
      know. Material will be presented from the slides, but also lots
      of hands on activities
      
      4. Your work is expected to be your own. Google is a useful tool
      to find solutions, and resuse is part of coding. However, you need to work on understanding the
      steps in your code so what you turn in should be a reflection of
      your own efforts.
      
      ---
      # Homework
      1. Turned in via iLearn.

2. Code can be submitted if a single script or you should plan
      to make a (github)[http://github.com] repository for the class and point me to your answers
      in a particular sub-folder.

3. Homework is due BEFORE class on the Wednesdays it is due. The next homework will be posted on Friday at latest.     
      ---

# Projects
      1. Topics to be selected from a set of options. Alternatives _can_ be pursued but you need to have a clear idea of what you will do.
      
      2. Project teams will be 3-4 individuals working together.
      
      3. A presentation will be made by each team - last day of class but may have to extend class time to accomodate this. Or present during finals week.
      4. A final report with the details
      
      4. The report needs to detail what each person's contribution is to the project.