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.

*Edit March 12* Be sure to look at the comments, especially the commentary on Hacker News - you can supercharge the find|xargs idea by using find|parallel instead. --- Do you ever discover a trick to do something better, faster, or easier, and wish you could reclaim all the wasted time and effort before your discovery?

I get a lot of requests in the core about running a "pathway analysis." Someone ran a handful of gene expression arrays, or better yet, ran an RNA-seq experiment (with replicates!). These, and many other kinds of high-throughput assays (GWAS, ChIP-seq, etc.) result in a list of genes and some associated p-value, fold change, or other statistic. Here's some R code to download public data from a study on susceptibility to colorectal cancer.

I direct the Bioinformatics Core at the University of Virginia, and I'm hiring. Visit this link on the UVA Jobs website for more information. Here's the description: I'm Hiring - Bioinformatics Analyst in the UVA Bioinformatics CoreGetting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Kevin White from the University of Chicago will be giving a special guest lecture at NCI next week on systems biology approaches to mine genomics data for biomarkers and therapeutic targets. The lecture will be available online as a videocast.

Lately I've been using the limma package often for analyzing microarray data. When I read in Affy CEL files using ReadAffy(), the resulting ExpressionSet won't contain any featureData annotation. Consequentially, when I run topTable to get a list of differentially expressed genes, there's no annotation information other than the Affymetrix probeset IDs or transcript cluster IDs.

The Galaxy Project started using CiteULike to organize papers that are about, use, or reference Galaxy. The Galaxy CiteULike group is open to any CUL user, and once you join, you can add papers to the group, assign tags, and rate papers.