Thanks to the excellent work of Lucia Hindorff and colleagues at NHGRI, the GWAS catalog provides a great reference for the cumulative results of GWAS for various phenotypes.  Anyone familiar with GWAS also likely knows about dbGaP – the NCBI repository for genotype-phenotype relationships – and the wealth of data it contains.

I just read an interesting paper on pathogen discovery using next-generation sequencing data, recommended to me by Nick Loman. A previously described algorithm (PathSeq, Kostic et al) for discovering microbes by deep-sequencing human tissue uses computational subtraction, whereby the initial collection of reads is depleted of human DNA by consecutive alignment to the human reference using MAQ and BLAST.

The ENCODE project continues to generate massive numbers of data points on how genes are regulated.  This data will be of incredible use for understanding the role of genetic variation, both for altering low-level cellular phenotypes (like gene expression or splicing), but also for complex disease phenotypes.  While it is all deposited into the UCSC browser, ENCODE data is not always the easiest to access or manipulate.

A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy.

I'm a huge supporter of the Free and Open Source Software movement. I've written more about R than anything else on this blog, all the code I post here is free and open-source, and a while back I invited you to steal this blog under a cc-by-sa license. Every now and then, however, something comes along that just might be worth paying for.

A few weeks ago I showed you how to convert gene IDs with BioMart. Yesterday I hosted a workshop on the Ensembl Genome Browser, given by Dr. Bert Overduin from EBI-EMBL. He gave several examples of very useful tasks that you can do very quickly and easily using BioMart. One, in particular, is something that I'm doing for a client in the core right now.

If you're doing any kind of big data analysis - genomics, transcriptomics, proteomics, bioinformatics - then unless you've been on vacation the last few weeks you've no doubt heard about the NSF/NIH BIGDATA  Initiative (here's the NSF solicitation and here's the New York Times article about the funding opportunity). The solicitation "aims to advance core scientific and technological means of managing, analyzing, visualizing, and

I was reading through a paper on comparative ChIP-Seq when I found this awk gem that lets you get some very basic stats very quickly on next generation sequencing reads. To use, simply cat the fastq file (or gunzip -c) and pipe that to this awk command: cat myfile.fq | awk '((NR-2)%4==0){read=$1;total++;count[read]++}END{for(read in count){if(!max||count[read]>max) {max=count[read];maxRead=read};if(count[read]==1){unique++}};print

There were lots of interesting developments this month that didn't work their way into a full blog post. Here is an incomplete list of what I've been tweeting about over the last few weeks.

I get asked frequently how to convert from one gene identifier to another. This can be tricky, especially when relying on gene symbols, as Will pointed out in a previous post a few years ago. There are several tools that can do this, including DAVID and the previously mentioned new Biomart ID Converter, but I still prefer using the Ensembl Biomart for this because of its added flexibility and annotation.

GGD has a new look. I was inspired by Gina Trapani (Smarterware, Lifehacker) to remove any extra lines, links, and other "ink" that doesn't serve any purpose, and I hope the site appears cleaner and easier to read. I also wanted the extra horizontal space for larger images and avoid the dreaded side-scrolling in posts with lots of code like this one.