Content Comparison

Your Instructors

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• Anna Battenhouse

  , Associate Research Scientist, Iyer Lab

  , abattenhouse@utexas.edu,
  Biomedical Research Computing Facility Manager, and Marcotte lab staff

  • BA English literature, 1978

  • Commercial software development 1982 –

    2005

    2007

  • Joined Iyer Lab 2007 (“retirement career”)

  • BS Biochemistry, UT Austin, 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
  • ???

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• • Joined the Biomedical Research Computing Facility (BRCF) and Marcotte Lab 2017
  • Also affiliated with
    • Bioinformatics Consulting Group (BCG)
    • Genome Sequencing and Analysis Facility (GSAF)
    • Cryo Electron Microscopy core facility (CryoEM)
    • Edward Marcotte lab
• Matt Bramble, matthew.bramble@austin.utexas.edu,
  Associate Research Scientist, Bioinformatics Consulting Group
  • Master’s degrees from UT Austin in Molecular Biology and Statistics
  • 10 years of experience with R and Python
  • Recently joined the CBRS Bioinformatics Consulting Group after six years at MD Anderson Cancer Center analyzing a wide range of NGS epigenomics data
  • Areas of expertise include: Hi-C (chromatin conformation) analysis, mouse somatic variant analysis, and single cell RNAseq analysis

About the Iyer Lab (where Anna learned NGS)

http://iyerlab.org/ Dr. Vishy Iyer, PI	Image Added
Main focus is functional genomics large-scale

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transcriptional reprogramming in response to diverse stimuli Encode consortium collaborator

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works in human and yeast
Research methods include microarrays (Dr. Iyer was co-inventor)	Image Modified
high-throughput sequencing (since 2007) especially ChIP-seq, RNA-seq also

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miRNA-seq, RIP-seq, MNase-seq ...

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>2,000 NGS datasets

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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.

Asking questions

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

For online attendees, you can also post your question to the Zoom chat. We'll sometimes use breakout rooms when troubleshooting problems you run into, if so, TA Matt Bramble will assign you to one.

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!

Making mistakes and running into problems is key to learning the Linux command line! It is not only expected – it is encouraged .

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 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:

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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 tool basics – the
  • The first few things you'll do after receiving raw sequences
    
    • raw sequence QC and 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
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  • 5 days to become experts over time
  • Show some "best practices" for working with NGS data

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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) R, python, perl Management – making order out of chaos one part organization one part data wrangling Adoption of best practices is critical!

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Large and growing datasets

NGS methods procude produce staggering amounts of data!

Typical dataset these days

• yeast:  5 – 20 million reads
• human:  20 – 100 250 million reads (~5 - 8 million for TagSeq)
• single end (SE) or paired end (PE), length 75 50 – 100 bases300 bases (100 or 150 typical)

The initial fastq 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!

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Progression of Iyer Lab

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datasets over time:

• 2008 – Yeast heat shock remodeling of chromatin
  • 2 yeast datasets
  • less than 2 million readssequences
• 2010 – Allelic bias in CTCF binding
  • 13 CTCF datasets from 3 GM cell lines
  • ~200 million readssequences
• 2012 – Analysis of Transcription factor data analysis (ENCODE2)
  
  • 32 ChIP-seq datasets gathered over 3 years (3 TFs across 11 cell lines
  • 32 datasets gathered over 3 years
  • ~ 1 billion reads
  2014 – QTL
  • )
  • ~ 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 readssequences
• in progress 2018 – Functional analysis of glioblastoma tumors and cell lines
  • > 300 datasets so far
  • > 17 billion reads

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• • nearly 500 datasets in total (ChIP-seq, RNAseq, miRNAseq, 4C, exome/genome sequencing)
  • > 22 billion sequences