n0b3l1a: October 2014

Friday, October 10, 2014

R Graph Catalog

http://lsi.ubc.ca/resources/omics-phenotyping-portal/#R_Graph_Catalog

Statistics Training and Online Resources

R Graph Catalog

http://shinyapps.stat.ubc.ca/r-graph-catalog/

Recommended by Stefanie Butland, LSI

The R Graph Catalog is a visual index of over 100 graphs from the excellent book "Creating More Effective Graphs" by Naomi Robbins. Click on a graph thumbnail and you'll see the figure AND all the code necessary to reproduce the figure exactly with ggplot2, the R package written by Hadley Wickham. This is a resource for people who want to make a good graph and kind of know what it should look like … but they could really use an example to get started!

You can get the code for ALL figures and the infrastructure that makes the app from this repository on GitHub:https://github.com/jennybc/r-graph-catalog

The R Graph Catalog is maintained by Dr Jennifer Bryan, UBC Department of Statistics, and the initial work was facilitated by an NSERC Undergraduate Student Research Award to Joanna Zhao.

8 Realities of the Sequencing GWAS

http://massgenomics.org/2014/03/gwas-sequencing-realities.html

For several years, the genome-wide association study (GWAS) has served as the flagship discovery tool for genetic research, especially in the arena of common diseases. The wide availability and low cost of high-density SNP arrays made it possible to genotype 500,000 or so informative SNPs in thousands of samples. These studies spurred development of tools and pipelines for managing large-scale GWAS, and thus far they’ve revealed hundreds of new genetic associations.

As we all know, the cost of DNA sequencing has plummeted. Now it’s possible to do targeted, exome, or even whole-genome sequencing in cohorts large enough to power GWAS analyses. While we can leverage many of the same tools and approaches developed for SNP array-based GWAS, the sequencing data comes with some very important differences.