Content Comparison

Your Instructors

• 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, UT Austin, 2013

• Amelia Weber Hall, PhD, Iyer Lab, ameliahall@utexas.edu

  • BS Molecular Genetics, 2007 (University of Rochester)

  • PhD Microbiology, 2017 (University of Texas at Austin)

  • Laboratory Technician at UT 2007-2010

• Dakota Derryberry, M.S., dakotaz@utexas.edu

  • BA Biology, University of Chicago, 2009

  • MS Computational Biology, University of Texas at Austin, 2017

• Benni Goetz, M.S., (Research Engineering/Scientist Associate III), benni@utexas.edu

  • joined the Bioinformatics Consulting Group in 2012

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 > 1,700 800 NGS datasets

Communication

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• Hands-on, tutorial style – learn by doing
  • common bioinformatics tools & file formats
• Introduce NGS vocabulary
  • both high-level view and practice with specific tools
• Cover the NGS basics
  • the first few things you'll do after receiving raw sequences
    
    • raw sequence preparation
    • alignment to reference
    • basic alignment analysis
• Understand and practice required skills
  • Get you comfortable with Linux and TACC – your best "frenemies"
  • Make you self-sufficient enough in 4 days to become experts over time
  • Show some "best practices" for working with NGS data

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• yeast:  5 – 20 million reads
• human:  20 – 100 250 million reads
• paired end, length 75 – 100 250 bases

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

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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
• 2017 (in progress review) – Functional analysis of glioblastoma tumors and cell lines
  • > 400 datasets so far nearly 500 datasets in total (ChIP-seq, RNAseq, miRNAseq, 4C, exome/genome sequencing)
  • > 22 billion sequences

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