Introduction

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/ 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 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
  • 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 in 4 days to become experts over time
  • Show some "best practices" for working with NGS data

NGS Challenges

Diverse skill set requirements

Analysis – making sense of raw data one part bioinformatics and statistics one part scripting / programming Linux command line bash scripting 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 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 datasets over time:

• 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, exome/genome sequencing)
  • > 20 billion sequences