Content Comparison

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
  • 5th year Cell & Molecular Biology graduate student
  • BA Biology, University of Chicago, 2009
• Rayna Harris, Graduate Student, Hofmann lab, rayna.harris@utexas.edu
  
  • Serves as Education and Outreach coordinator for CCBB

About the Iyer Lab

http://iyerlab.org/ Dr. Vishy Iyer, PI
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 > 1,500 700 NGS datasets

Communication

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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

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Analysis – making sense of raw data one part bioinformatics and statistics one part scripting / programming Linux command line High Performance Computing (TACC) bash scripting (grep, awk, sed) R, python, perl Management – making order out of chaos one part organization one part data wrangling Adoption of best practices is critical!

Large and growing datasets

NGS methods procude produce staggering amounts of data!

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• 2008 – Yeast heat shock remodeling of chromatin
  • 2 yeast datasets
  • less than 2 million sequences
• 2010 – Allelic bias in CTCF binding
  • 13 CTCF datasets from 3 GM cell lines
  • ~200 million sequences
• 2012 – Transcription factor data analysis (ENCODE2)
  
  • 32 ChIP-seq datasets gathered over 3 years (3 TFs across 11 cell lines)
  • ~ 1 billion sequences
• 2013 – miRNA overexpression effects
  • 42 RNAseq datasets (7 conditions)
  • ~ 2.6 billion sequences
• 2014 – eQTL analysis of CTCF binding
  • 52 very deeply sequenced CTCF datasets
  • ~ 8 billion sequences
• in progress – Functional analysis of glioblastoma tumors and cell lines
  • > 400 datasets so far (ChIP-seq, RNAseq, miRNAseq, 4C, exome/genome sequencing)
  • > 20 22 billion sequences