Introduction

Communication

Post its

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

Pink post-it – I need a bit of help.

Conventions

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:

Your Instructors

• Anna Battenhouse, Associate Research Scientist, abattenhouse@utexas.edu

  • BA English literature, 1978

  • Commercial software development 1982 – 2005

  • Joined Iyer Lab 2007 (“retirement career”)

  • BS Biochemistry, UT Austin, 2013

  • Joined Marcotte lab and Biomedical Research Support Facility (BRCF) in summer 2017

• Haridha Shivram, haridh@utexas.edu

  • 6th year graduate student in the Iyer Lab

• Claire McWhite, claire.mcwhite@utexas.edu

  • 5th year graduate student in the Marcotte Lab

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 nearly 2,000 NGS datasets

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 enough 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 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 produce staggering amounts of data!

Typical dataset these days

• yeast:  5 – 20 million reads
• human:  20 – 250 million reads
• single or paired end, length 75 – 250 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
• 2018 – Functional analysis of glioblastoma tumors and cell lines
  • nearly 500 datasets in total (ChIP-seq, RNAseq, miRNAseq, 4C, exome/genome sequencing)
  • > 22 billion sequences