Like most bioinformatics nerds (or anyone with a facebook account), I’m fascinated by networks. Most people immediately think of protein-protein interaction networks, or biological pathways when thinking about networks, but sometimes representing a problem as a network makes solving problems easier. Recently, some collaborators from the PAGE study had a list of a few hundred SNPs gathered from multiple loci across the genome.

I just accepted an offer for a faculty position at the University of Virginia in the Center for Public Health Genomics / Department of Public Health Sciences. Starting in October I will be developing and directing a new centralized bioinformatics core in the UVA School of Medicine. Over the next few weeks I'm taking a much-needed vacation next door in Kauai and then packing up for the move to Charlottesville.

I just read Gregory Cooper and Jay Shendure's review "Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data" in Nature Reviews Genetics. It's a good review about how to narrow down deleterious disease-causing variants from many, many variants throughout the genome when statistics and genetic information alone isn't enough.

Daniel Cook in Jeff Murray's lab at the University of Iowa put together this handy Excel template for keeping track of how samples from four 96-well plates are interleaved to configure a single 384-well plate using robotic liquid handling systems, like the Hydra II. Paste in lists of samples on your 96-well plates: And you'll get out a map of how the 384-well plate layout: And a summary list: You can download the Excel file

I was recently contacted by a couple of German biologists working on a project evaluating opinions on sharing raw data from DTC genetic testing companies like 23andme. A handful of people like the gang at Genomes Unzipped, the PGP-10, and others at SNPedia have released their own genotype or sequencing data into the public domain. As of now, data like this is scattered around the web and most of it is not attached to any phenotype data.

I mentioned BioStar in a previous post about getting all your questions answered. I can't emphasize enough how helpful the BioStar and other StackExchange communities are. Whenever I ask a statistics question on CrossValidated or a programming question on StackOverflow I often multiple answers within 10 minutes.

Peter Visscher and colleagues have recently published a flurry of papers employing a new software package called GCTA to estimate the heritability of traits using GWAS data (GCTA stands for Genome-wide Complex Trait Analysis -- clever acronymity!). The tool, supported (and presumably coded) by Jian Yang is remarkably easy to use, based in part on the familiar PLINK commandline interface.

Your Rprofile is a script that R executes every time you launch an R session.

I haven't posted much here recently, but here is a roundup of a few of the links I've shared on Twitter (@genetics_blog) over the last two weeks. Here is a nice tutorial on accessing high-throughput public data (from NCBI) using R and Bioconductor. Cloudnumbers.com , a startup that allows you to run high-performance computing (HPC) applications in the cloud, now supports the previously mentioned R IDE, RStudio.

I just discovered a handy function in R to produce a scatterplot matrix of selected variables in a dataset. The base graphics function is pairs(). Producing these plots can be helpful in exploring your data, especially using the second method below. Try it out on the built in iris dataset.

Sequencing company Complete Genomics recently made available 69 ethnically diverse complete human genome sequences: a Yoruba trio; a Puerto Rican trio; a 17-member, 3-generation pedigree; and a diversity panel representing 9 different populations. Some of the samples partially overlap with HapMap and the 1000 Genomes Project. The data can be downloaded directly from the FTP site.