2021 Introduction

Your Instructors

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

  • BA English literature, 1978

  • Commercial software development 1982 – 2007

  • Joined Iyer Lab 2007 (“retirement career”)

  • BS Biochemistry, UT Austin, 2013

  • Joined the Biomedical Research Support Facility (BRCF) and Marcotte Lab summer 2017
  • Also affiliated with
    • Bioinformatics Consulting Group (BCG)
    • Genome Sequencing and Analysis Facility (GSAF)

• Rachael Cox, rachaelcox@utexas.edu

  • BS Organic Chemistry, 2013
  • Junior R&D chemist 2014-2018
  • Started PhD in Biochemistry in 2018

  • Third year graduate student in the Marcotte Lab 

  • Research Interests: Comparative multi-omics, systems biology, and protein complex evolution

About the Iyer Lab (where Anna learned NGS)

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, RNA-seq also miRNA-seq, RIP-seq, MNase-seq ... have ~2,000 NGS datasets

Communication

Asking questions

Feel free to ask questions any time during the instructor's lecture and demonstrations.

You can also post your question to the Zoom chat.

Breakout rooms

We'll sometimes use breakout rooms when working on short assignments, and when troubleshooting problems you run into. As you login to the Zoom, you'll be assigned to a breakout rooms where you can join TA Rachael Cox for assistance.

Getting help

Since most folks are new to the Linux command line, we expect you to run into problems! Please let us know if you're having difficulties in the Zoom chat. 

Making mistakes and running into problems is key to learning the Linux command line! It is not only expected – it is encouraged . So once you tell us you're having an issue and get our attention, we encourage you to share your screen so everyone can benefit from shared troubleshooting.

If you'd prefer not to share your screen with with the class, the TA may ask you to join a breakout room to help you, depending on the issue.

Conventions

If you see a block of text like this:

ls -h

it means, type the command ls -h into a terminal window, hit Enter, 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 enough in 5 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