Your Instructors

Most of us are members (or alumni) of the functional genomics lab of Vishwanath Iyer, UT Austin.

• Anna Battenhouse, Associate Research Scientist, Iyer Lab, abattenhouse@utexas.edu
  • BA English literature, 1978
  • Commercial software development 1982 – 2005
  • Joined Iyer Lab 2007 (“retirement career”)
  • BS Biochemistry, 2013
• Amelia Weber Hall, Graduate Student, Iyer Lab, ameliahall@utexas.edu
  • 5th year Microbiology graduate student
  • Laboratory Technician at UT 2007-2010
  • BS Molecular Genetics, 2007
• Nathan Abell, Research Assistant, Xhemalce Lab, abell.nathan@gmail.com
  • Undergraduate researcher in Iyer Lab 2011-2013
  • BS Molecular Biology, UT, 2013
  • Research Assistant
• Dakota Derryberry, Graduate Student, Wilke Lab, dakotaz@utexas.edu
  • ???

About the Iyer Lab

http://iyerlab.org/

• Main focus is functional genomics
  • large-scale transciptional reprogramming in response to diverse stimuli
  • Encode consortium collaborator
  • work in human and yeast
• Research methods include
  • microarrays (Dr. Iyer was co-inventor)

• • • 
  • high-throughput sequencing (since 2007)
    
    • especially ChIP-seq
    • also RNA-seq, RIP-seq, MNase-seq ...
    • we now have > 1,500 NGS datasets

    • 

Communication

Post its

Green post-it – I'm good at the moment.

Pink post-it – I need a bit of help.

Conventions

Text that you find in courier font refers to a program or file name on a computer.

If you see a block of text like this:

ls -h

it means, "type the command ls -h into a terminal window, hit return, and see what happens".

We intend this course to offer as much self-learning as possible. Consequently, you'll find many sections like this - click on the triangle to expand them:

and some sections like this:

Course goals

• Hands-on, tutorial style – learn by doing
• Cover the NGS tool basics – the first few things you'll do after receiving raw sequences
• Get you comfortable with Linux and TACC – your best "frenemies"
• Make you self sufficient in 4 days to become experts over time
• Show some "best practices" for working with NGS data

NGS Challenges

Large and growing datasets

NGS methods procude staggering amounts of data!

Typical dataset these days

• yeast:  5 – 20 million reads
• human:  20 – 100 million reads
• paired end, length 75 – 100 bases

The initial fastq files are big (100s of MB to GB) – and they're just the start.

• Organization and naming conventions are critical.
• Your data can get out of hand very quickly!

progression of Iyer Lab ChIP-seq datasets over time:

• 2008 – Yeast heat shock remodeling of chromatin
  • 2 yeast datasets
  • less than 2 million reads
• 2010 – Allelic bias in CTCF binding
  • 13 CTCF datasets from 3 GM cell lines
  • ~200 million reads
• 2012 – Analysis of 3 TFs across 11 cell lines
  • 32 datasets gathered over 3 years
  • ~ 1 billion reads
• 2014 – QTL analysis of CTCF binding
  • 52 very deeply sequenced CTCF datasets
  • ~ 8 billion reads
• in progress – Functional analysis of glioblastoma tumors and cell lines
  • > 400 datasets so far
  • > 20 billion reads