About a year after setting up my laboratory, an observation suddenly hit me. All the job applicants were biologists who wanted to do bioinformatics. I was myself trained as an experimental biologist and started bioinformatics during my post-doc. They saw in my laboratory the opportunity to do the same. Indeed, “how did you become a bioinformatician?” is a question that I hear very often.
For lack of a better plan, most people grab a book about Linux or sign up for a Coursera class, try to do a bit every day and... well, just learn bioinformatics. I have seen extremely few people succeed this way. The content inevitably becomes too difficult, motivation decreases and other commitments take over. I will not lie, self-learning bioinformatcs is hard and it is frustrating... but it can be fun if you know how to do it. And most importantly, if you understand your worst enemy: yourself.
Here is a small digest of how it happened for me. I do not mean that this is the only way. I simply hope that this will be useful to those who seriously want to dive into bioinformatics.
Step 1. Get out of your...
The story of this post begins a few weeks ago when I received a surprising email. I have never read a scientific article giving a credible account of a research process. Only the successful hypotheses and the successful experiments are mentioned in the text — a small minority — and the painful intellectual labor behind discoveries is omitted altogether. Time is precious, and who wants to read endless failure stories? Point well taken. But this unspoken academic pact has sealed what I call the curse of research. In simple words, the curse is that by putting all the emphasis on the results, researchers become blind to the research process because they never discuss it. How to carry out good research? How to discover things? These are the questions that nobody raises (well, almost nobody).
Where did I leave off? Oh, yes... in my mailbox lies an email from David Lipman. For those who don’t know him, David Lipman is the director of the NCBI (the bio-informatics spearhead of the NIH), of which PubMed and GenBank are the most famous children. Incidentally, David is also the creator of BLAST. After a brief exchange on the topic of my previous...
In July 1982, paleontologist Steven Jay Gould was diagnosed with cancer. Facing a median prognosis of only 8 months survival, he used his knowledge of statistics to prepare for the future. As he explains in The Median Isn’t the Message, if half of the patients died of this rare case of mesothelioma within 8 months, those who did not had much better survival. Evaluating his own chances of being in the “survivor” group as high, he planned for long term survival and opted out of the standard treatment. He died 20 years later, from an unrelated disease.
If not the median, then what is the message? Statistics put a disproportionate emphasis on the typical or average behavior, when what matters is sometimes in the extremes. This general blindness to the extremes is responsible for a dreadful lot of confusion in the bio-medical field. One of my all time favorite traps is the extreme value fallacy. Nothing better than an example will explain what it is about.
June babies and anorexia