Your Instructors
- 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
- Dakota Derryberry, Graduate Student, Wilke Lab, dakotaz@utexas.edu
- 5th year Cell & Molecular Biology graduate student
- BA Biology, University of Chicago, 2009
- Rayna Harris, Graduate Student, Hofmann lab, rayna.harris@utexas.edu
- Serves as Education and Outreach coordinator for CCBB
About the Iyer Lab
Dr. Vishy Iyer, PI | |
Main focus is functional genomics
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Research methods include
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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
If you see a block of text like this:
Example code block
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
- the first few things you'll do after receiving raw sequences
- 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
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Large and growing datasets
NGS methods produce 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, 4C, exome/genome sequencing)
- > 22 billion sequences