Seminar: Pathway-based analysis for genome-wide association studies

Vanderbilt Epidemiology Center, Institute for Medicine and Public Health presents:

"Pathway-based analysis for genome-wide association studies"

Steven Chen Ph.D
Assistant Professor of Biostatistics

Tuesday, March 16, 2010
9:00 AM - 10:00 AM
2525 West End Avenue 6th Floor Boardroom

Papers from March 8, 2010 Journal Club

Here are the papers we talked about in yesterday's Journal Club:

Genome Biol. 2009; 10(11): R134
Searching for SNPs with cloud computing.
Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL.

Table of Contents, Nature Methods, Visualization Supplement.

Am J Hum Genet. 2010 Feb 12; 86(2): 113-25
Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability.
Ruano D, Abecasis GR, Glaser B, Lips ES, Cornelisse LN, de Jong AP, Evans DM, Smith DG, Timpson NJ, SMit AB, Heutink P, Verhage M, Posthuma D.

Kathy Giacomini: Personalizing Anti-diabetic Drug Therapy

This Thursday's discovery lecture looks interesting. In case you missed last week's Nobel laureate, you can watch the recording from Cech's talk or any of the previous lectures in the discovery series here.

Kathy Giacomini
Professor and Co-Chair Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco

March 11, 2010
"Personalizing Anti-Diabetic Drug Therapy"

Sponsor: Division of Clinical Pharmacology and Department of Pharmacology

208 Light Hall / 4:00 p.m. (CST)

Nature Methods: Visualization

Check out this month's table of contents in  Nature Methods. It contains a series of five commissioned Reviews discuss the challenges of visualizing biological data and the visualization tools available to biologists working with genomes, alignments and phylogenies, macromolecular structures, images and systems biology data.

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Foreword

Supplement on visualizing biological data - pS1

Daniel Evanko

doi:10.1038/nmeth0310-S1

Full Text - Supplement on visualizing biological data | PDF (188 KB) - Supplement on visualizing biological data

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Commentary

Visualizing biological data—now and in the future - ppS2 - S4

Seán I O'Donoghue, Anne-Claude Gavin, Nils Gehlenborg, David S Goodsell, Jean-Karim Hériché, Cydney B Nielsen, Chris North, Arthur J Olson, James B Procter, David W Shattuck, Thomas Walter & Bang Wong

doi:10.1038/nmeth.f.301

Methods and tools for visualizing biological data have improved considerably over the last decades, but they are still inadequate for some high-throughput data sets. For most users, a key challenge is to benefit from the deluge of data without being overwhelmed by it. This challenge is still largely unfulfilled and will require the development of truly integrated and highly useable tools.

Abstract - Visualizing biological data[mdash]now and in the future | Full Text - Visualizing biological data—now and in the future | PDF (336 KB) - Visualizing biological data—now and in the future

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Reviews

Visualizing genomes: techniques and challenges - ppS5 - S15

Cydney B Nielsen, Michael Cantor, Inna Dubchak, David Gordon & Ting Wang

doi:10.1038/nmeth.1422

Abstract - Visualizing genomes: techniques and challenges | Full Text - Visualizing genomes: techniques and challenges | PDF (993 KB) - Visualizing genomes: techniques and challenges | Supplementary information

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Visualization of multiple alignments, phylogenies and gene family evolution - ppS16 - S25

James B Procter, Julie Thompson, Ivica Letunic, Chris Creevey, Fabrice Jossinet & Geoffrey J Barton

doi:10.1038/nmeth.1434

Abstract - Visualization of multiple alignments, phylogenies and gene family evolution | Full Text - Visualization of multiple alignments, phylogenies and gene family evolution | PDF (867 KB) - Visualization of multiple alignments, phylogenies and gene family evolution | Supplementary information

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Visualization of image data from cells to organisms - ppS26 - S41

Thomas Walter, David W Shattuck, Richard Baldock, Mark E Bastin, Anne E Carpenter, Suzanne Duce, Jan Ellenberg, Adam Fraser, Nicholas Hamilton, Steve Pieper, Mark A Ragan, Jurgen E Schneider, Pavel Tomancak & Jean-Karim Hériché

doi:10.1038/nmeth.1431

Abstract - Visualization of image data from cells to organisms | Full Text - Visualization of image data from cells to organisms | PDF (1,682 KB) - Visualization of image data from cells to organisms

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Visualization of macromolecular structures - ppS42 - S55

Seán I O'Donoghue, David S Goodsell, Achilleas S Frangakis, Fabrice Jossinet, Roman A Laskowski, Michael Nilges, Helen R Saibil, Andrea Schafferhans, Rebecca C Wade, Eric Westhof & Arthur J Olson

doi:10.1038/nmeth.1427

Abstract - Visualization of macromolecular structures | Full Text - Visualization of macromolecular structures | PDF (2,317 KB) - Visualization of macromolecular structures | Supplementary information

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Visualization of omics data for systems biology - ppS56 - S68

Nils Gehlenborg, Seán I O'Donoghue, Nitin S Baliga, Alexander Goesmann, Matthew A Hibbs, Hiroaki Kitano, Oliver Kohlbacher, Heiko Neuweger, Reinhard Schneider, Dan Tenenbaum & Anne-Claude Gavin

doi:10.1038/nmeth.1436

Abstract - Visualization of omics data for systems biology | Full Text - Visualization of omics data for systems biology | PDF (1,469 KB) - Visualization of omics data for systems biology | Supplementary information

Searching for SNPs with cloud computing

Suppose you have billions of reads from a hot new sequencing machine and you want to simultaneously align these reads and call SNPs very quickly on the cheap. Check out an open source tool called Crossbow and the recent paper in Genome Biology.  Crossbow is a Hadoop-based software tool that combines the speed of the short read aligner Bowtie with the accuracy of the SNP caller SOAPsnp to perform alignment and SNP calling for multiple human whole-genome datasets per day. In the demonstration in the paper, the authors aligned and called SNPs from 2.7 billion short reads from a Han Chinese male with 98% concordance to the calls from an Illumina genotyping chip. The whole process took 3 hours on a 320-core parallel computing cluster rented from the Amazon Elastic Compute Cloud (EC2) for a total cost of $85. Since everything is open-source, there should be nothing stopping you from downloading all the necessary software and running it on your own cluster if you have access to one.

Crossbow: Genotyping from short reads using cloud computing

Arrange multiple ggplot2 plots in the same image window

In a previous tutorial I showed you how to create plots faceted by the level of a third variable using ggplot2. A commenter asked about using faceted plots and viewports and reminded me of this function I found in the ggplot2 Google group. The arrange function below is similar to using par(mfrow=c(r,c)) in base graphics to put more than one plot in the same image window.



The basic idea is that you assign ggplot2 plots to an object, and then use the arrange function to display two or more. Here's an example. First copy and paste the code above (or put in your Rprofile). Next install and/or load ggplot2 as described in a previous ggplot2 tutorial.

# Load the diamonds dataset
data(diamonds)

# Create a histogram, assign to "plot1"
plot1 <- qplot(price,data=diamonds,binwidth=1000)

# Create a scatterplot
plot2 <- qplot(carat,price,data=diamonds)

# Arrange and display the plots into a 2x1 grid
arrange_ggplot2(plot1,plot2,ncol=1)

And here's what you should get:

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Wiley Essential Biochemistry Online

I joined the Ritchie Lab back in 2007, and even though it's only been three years away from the bench, I've forgotten much of what I learned back in biochem classes.  I'm giving a talk on lipid genetics next week, and I found the Wiley Essential Biochemistry website very helpful for brushing up on some basic lipoprotein biology. There are 27 chapters covering a broad range of topics from enzyme kinetics to phosphofructokinase regulation. Many of them have short animations and optional exercises to test your knowledge. It's a great resource for brushing up on some fundamental biochemical concepts when you need to.

Seminar: GWAS, Lipid Genetics, and EMR-Linked Biobanks

Time for a little shameless self-promotion. I'll be giving a talk in genetics interest group next week.

"Using GWAS in an EMR-linked biobank to explore genetic and environmental determinants of HDL cholesterol"

Thursday, March 11, 2010
Noon-1pm
206 PRB