[aheilbut]

I sympathize with the author, but this piece fails because many of the specific criticisms are off-base, and he's not trying to be at all constructive.

For example, it isn't true at all that microarray data is worthless. The early data was bad, and it was very over-hyped, but with a decade of optimization of the measurement technologies, better experimental designs, and better statistical methods, genome-wide expression analysis became a routine and ubiquitous tool.

The claim that sequencing isn't important is ridiculous. It's the scaffold to which all of biological research can be attached.

However:

There is a great deal of obfuscation, and reinventing well-known algorithms under different names (perhaps often inadvertently). There's also a lot of low-quality drivel on tool implementations or complete nonsense. This is driven largely by the need in academia to publish.

The other side of this problem is that in general, CS and computer scientists don't get much respect in biology. People care about Nature/Science/Cell papers, not about CS conference abstracts. Despite bioinformatics/computational biology not really being a new field anymore, the cultures are still very different.

[east2west]

No kidding about reinventing wheels. I once saw a manuscript based entirely on dot-product as 1-D least-square. I don't know what happened to it, but one reviewer called it a seminal event in GWAS.

Bioinformatics is hard, but too many careerists take advantage of difficulties and uncertainty to publish as many papers as they can get away with.