Content Comparison

Your Instructors

• Anna Battenhouse, Associate Research Scientist, Marcotte Lab and BRCF, abattenhouse@utexas.edu, abattenhouse@utexas.edu,
  Biomedical Research Computing Facility Manager, and Marcotte lab staff

  • BA English literature, 1978

  • Commercial software development 1982 – 20052007

  • Joined Iyer Lab 2007 (“retirement career”)

  • BS Biochemistry, UT Austin, 2013

  • J

• Haridha Shivram, 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

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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
Main focus is functional genomics large-scale transciptional transcriptional reprogramming in response to diverse stimuli Encode consortium collaborator work works in human and yeast
Research methods include microarrays (Dr. Iyer was co-inventor)
high-throughput sequencing (since 2007) especially ChIP-seq, RNA-seq also RNAmiRNA-seq, RIP-seq, MNase-seq ... we now have > 1,800 >2,000 NGS datasets

Communication

Post its

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

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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:

Course goals

• Hands-on, tutorial style – learn by doing
  • common Common bioinformatics tools & file formats
• Introduce NGS vocabulary
  • both high-level view and practice with specific tools
• Cover the NGS 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 4 5 days to become experts over time
  • Show some "best practices" for working with NGS data

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NGS Challenges

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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 (~5 - 8 million for TagSeq)
• single end (SE) or paired end (PE), length 75 50 – 250 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!

progression Progression of Iyer Lab datasets over time:

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