The key of success is to choose the right people for your team. That’s what everybody will tell you. But if you ask “how?”, things will get a little more complicated. Practitioners admit that this is a tough problem, and you cannot expect to win all the time. Google attempted to answer the question with the so-called Project Aristotle. After sifting a huge amount of data through the best algorithms, they concluded that you can ignore gender, age, culture, education and pretty much everything else... What makes a good team is not good people, it is good interactions.
This is great news, but it does not really answer the question of how to choose the right people for your team. How does one know that a candidate will nicely interact with the team? Those were the questions I had in mind when I had to assemble my scientific team. Like almost everyone, I did not have any kind of training in this regard and I was not really prepared. So I sought advice from everybody who would care to give me their opinion, may they be colleagues, friends or family members. In the process, I did...
According to the legend, King Midas got the sympathy of the Greek god Dionysus who offered to grant him a wish. Midas asked that everything he touches would turn into gold. At first very happy with his choice, he realized that he had brought on himself a curse, as his food turned into gold before he could eat it.
This legend on the theme “be careful what you wish for” is a cautionary tale about using powers you do not understand. The only “powers” humans ever acquired were technologies, so one can think of this legend as a warning against modernization and against the fact that some things we take for granted will be lost in our desire for better lives.
In data analysis and in bioinformatics, modernization sounds like “Big Data”. And indeed, Big Data is everything we asked for. No more expensive underpowered studies! No more biased small samples! No more invalid approximations! No more p-hacking! Data is good, and more data is better. If we have too much data, we can always throw it away. So what can possibly go wrong with Big Data?
Enter the Big Data world and everything you touch turns...
Miguel Beato is one of my favourite scientists. We met at the CRG in Barcelona, where we both work and often collaborate. One of the most interesting things about Miguel is that for more than fifty years, he remained a pioneer in the field of steroid hormones. He embraced every scientific revolution and he keeps pushing scientific progress forward with unmatched panache. I figured I would collect some of his thoughts on the future of science and other topics that I enjoy talking with him about.
Guillaume Filion: What do you think has been the most important revolution in science since the beginning of your career?
Miguel Beato: The transition from analysing single events to global events in the cell. Actually, changing the microscope for statistics.
GF: Why is this so important?
MB: Because we can for the first time look at cells, even at organisms. We have a tool to measure changes and variations that was not available before. This is what enables the kind of approach that we all have at CRG. The only way to study processes is to use networks, circuitry of things you know are connected, and try to understand things this way...
The code is written in a very naive style, so you should not use it as a reference for good C code. Once again, the purpose is to highlight the mechanisms of the algorithm, disregarding all other considerations. That said, the code runs so it may be used as a skeleton for your own projects.
The code is available for download as a Github gist. As in the second part, I recommend playing with the variables, and debugging it with gdb to see what happens step by step.
Constructing the suffix array
First you should get familiar with the first two parts of the tutorial in order to follow the logic of the code below. The file
learn_bwt_indexing_compression.c does the same thing as in the second part. The input, the output and the logical flow are the same, but the file is different in many details.
We start with the definition of the
The code runs, but I doubt that it can be used for anything else than demonstrations. First, it is very naive and hard to scale up. Second, it does not use any compression nor down-sampling, which are the mainsprings of Burrows-Wheeler indexes.
The code is available for download as a Github gist. It is interesting for beginners to play with...
Scientific models are more of an art than a science. It is much easier to recognize a good scientific model than to make one of our own. Like for an art, the best way to learn is to look at the work from the masters and take inspiration from them. One of the crown jewels of modern science is undoubtedly Darwin’s Theory of Evolution. I recently realized that I had no idea how Darwin stood against creationism and how he defended his view in regard of the doxa of his time. Digging into this topic turned out to be one the most important lessons I learned about the scientific method... and the lack of it.
Darwin touches vividly upon creationism at the end of “On the Origin of Species”. In his own words, he claims that
It is so easy to hide our ignorance under such expressions as the ‘plan of creation’, ‘unity of design’, etc, and to think that we give an explanation when we only restate a fact.
What strikes me here is that he does not accuse the ‘plan of creation’ and the ‘unity of design’ of not being proper scientific concepts. The real...
In my first years as a group leader, I had the chance to interview PhD candidates in panels at international calls for students. I quickly stopped interviewing students, but back then I was very surprised that the top candidates often proved less productive than those we had ranked mediocre. How was this possible at all? Panels are unbiased, they combine multiple expertises, they allow for critical discussion, so they should be able to pick the best candidates... right?
It turns out to be less surprising than I thought. Now a little more familiar with the dangers of panel interviews, I decided to see what our colleagues from the academia have to say about it. This is of course where I should have started before interviewing PhD students, but better late than never. If you haven’t met them, let me introduce you to the flaws of the mighty recruitment panel interview...
1. The unproved efficiency of panels
A good place to start. Despite forty years of research, the benefit of recruitment panels over single evaluators is still debated. According to a review published in 2009
Findings to date suggest that panel interviews might not necessarily provide the psychometric...
For a little more than a year, my colleagues and me have been organizing peer coaching sessions for junior group leaders. A typical session consists of four to six of us, and we meet for one morning to discuss the most pressing issues. After a start-up training and some trial and error, we settled for a group coaching method that gave the best result. To give an idea, the “coachee” tells the chairman what he/she wants to solve, then follows a discussion where he/she explains the facts to the coaches who ask as many question as possible. Then the coaches analyze the situation, suggest solutions and make comments meanwhile the coachee has to remain silent and listen. Finally, the coachee summarizes what he/she heard and what steps he/she will take.
With this exercise, we learned a great deal about how to organize such peer coaching sessions in the academia and how to make the best of them, but this is not what this post is about. Instead, I would like to share more important lessons I have learned about working together and using the group as support and source of motivation.
Here is a discussion that I recently had with my colleague John. He approached me with the following request:
“I sent a manuscript to Nature and it is going quite well. Actually the reviewers are rather positive, but one of them asks us to justify better why we used a one-tailed t test to find the main result. What should I write in the methods section?
— It depends. Why did you use a one-tailed t test?
— Well, we first tried the standard t test, but it was borderline significant. My student realized that if we used the one-tailed t test, the result was significant so we settled for this variant. We specified this clearly in the text, and I am now surprised that I have to justify it. Isn’t it just an accepted variant of the t test?
— To be honest, I understand your confusion. The guidelines are rather ill-defined. Actually, Nature journals make it worse by requesting this information for every test, even for those that are only one-tailed like the chi-square.
— OK, but what should I do now? For instance, how do you justify using a one-tailed t...
There are many resources explaining how the Burrows-Wheeler transform works, but so far I have not found anything explaining what makes it so awesome for indexing and why it is so widely used for short read mapping. I figured I would write such a tutorial for those who are not afraid of the detail.
Say we have a sequencing run with over 100 million reads. After processing, the reads are between 20 and 25 nucleotide long. We would like to know if these sequences are in the human genome, and if so where.
The first idea would be to use
grep to find out. On my computer, looking for a 20-mer such as
ACGTGTGACGTGATCTGAGC takes about 10 seconds. Nice, but querying 100 million sequences would take more than 30 years. Not using any search index,
grep needs to scan the whole human genome, and this takes time...