Commentary: ChIP-chip vs ChIP-seq and $$

Ok, so with the rush to ChIP-seq and all the hype (much of it deserved) around “next-generation” sequencing generally, you might think that arrays are dead as used for ChIP (i.e. ChIP-chip).

I don’t think this is going to happen for simple cost reasons. For the near future, there will be lots of genome-scale ChIP studies and, for these, I strongly support ChIP-seq. It is a lot cheaper for better data. But I see a strong trend toward ChIP studies targeted toward specific biological questions and often questions requiring large sample numbers (e.g. epigenetic changes is cancer).

The financial math really isn’t that hard; with ChIP-seq running ~$5000 for external users and ChIP-chip running at $660 for external users (NimbleGen single arrays), it seems pretty clear that if a fair number of samples are involved, ChIP-chip is the way to go. That is, unless high-res whole genome coverage is absolutely necessary (usually not).

Furthermore, for taking chances on experiments, $660/sample is a lot more appealing on a lab budget than $5000/sample, particularly when you consider that, in the real world, even poor testing of a speculative idea is going to take 2 or 3 samples at minimum (=~$15,000 for ChIP-seq vs $1980 for ChIP-chip). A lot of labs can blow $2000; blowing $15,000 really hurts.

Given this analysis, it seems to me that NimbleGen should really push the low end of the market – in other words, try to get the cost even lower on a per sample basis (for fewer spots). I think they are on the right track with their multiplex arrays, but development of these has been disappointingly slow, and last time I looked, the cost structure around the 4plex with 70K/quadrant really wasn’t very attractive.

I may revisit this topic another time, but that is it for now.

I wish I had… started with python earlier…

So far, my bioinformatics work has used a melange of perl, R, and bash scripting. While this has worked pretty well, it does have limits. For one, it is very not portable (bash scripting). I’ve already had problems with distributing software.

I wanted something that I could distribute in an easier way, yet had the advantages of perl. I found Jython, which is Python-in-Java. For me, the big deal is not use of Java libraries, but rather that the language would compile to Java byte-code and hence would be easy to distribute.

But I found that Python is much more than this: the interactive environment, for one, makes me ok with not having my unix/linux toolbox when I am stuck on the windows side.

And Python has a lot of nice features for bioinformatics work, including convenient types like sets (as of version 2.4) and even comes with sqlite (which I have not used from python, but want to)…

Anyways, for now, I am a fan.

Anti-Lifehacker: Why Lifehacker is probably bad for you

Lifehacker is a website with (mostly) technological solutions for productivity – and it is super-popular.

Lifehacker sounds good – who doesn’t want to improve their productivity or upgrade the way they approach a problem?

But there are deep, but slightly subtle, problems with Lifehacker:

1. Lifehacker ignores the big cost of installing a new piece of software: time and energy.
2. Lifehacker does minimal testing of software – and never does the “I used it daily for 3 months” type of testing.
3. Lifehacker values newness (“newly available!”) over robust, well-tested, solutions.
4. Lifehacker does minimal comparative testing: if there are thirty “todo list” applications on the web, I want to know about the best ones – not just the names of all thirty. I really want someone to evaluate things for me.
5. Lifehacker focuses on free software but ignores one of the most important parts: mature software with a significant user base and robust support. Sure, I respect heroic, single person efforts. But I’d rather have a piece of software with strong, sustained support.

So what’s good about lifehacker?

1. It’s fun. That is, if you are a certain sort of person, it’s fun.
2. It does provide a snapshot – and repository – of new software developments in the productivity area.
3. It provides exposure for new software. And some of this software is probably great.

For me, I just worry about the time and energy… and the illusion that I am helping my productivity. So personally, I’ll spend my time writing these blog entries instead.