Galaxy, one of my favorite bioinformatics websites now offers the conversion of existing history into a work flow. Obviously source data (by UCSC or Ensembl) may be produced in the same format but everything else can then be delegated to a workflow – just a set of instructions how to modify your data.

The authors put a question mark at the end of the above statement while I would not hesitate to put an exclamation mark there. Writing this as a comment to a new study in the IJE they summarize the evidence that the ‘epidemic’ of asthma in Western countries has begun to decline – as hygiene standards are not declining this might indicate the end of the hygiene hypothesis. (Show me more…)

Having a free copy of the Lancet at the moment, I found a nice book review about “Dissent over Descent” by Steven Rose.

[He] takes a pleasure, which in part I share, in puncturing the often hyperbolic claims of natural scientists to be unimpeachable purveyors of absolute truth (Show me more…)

The experts in the field will immediately notice what I am suggesting here – an improved GWA plot that does not take into account p values alone but also effect sizes. I was experimenting some time with smile plots but finally ended with this bubble plot. Bubble size for 0.5<OR>2 is set to a minimum while all other ORs get increasing bubbles (BTW use for OR<1 a 1/OR transformation beforehand). Chromosomal colors are from a self defined palette using the colorRampPalette function in R which makes it look like pointillism art. The real question: Did the previous GWA p value screening miss some important effects? For example the important dot at x=4 and y=4?