This post is part of a series that interviews some notable bioinformaticians to get their views on various aspects of bioinformatics research. Hopefully these answers will prove useful to others in the field, especially to those who are just starting theirbioinformatics careers.

Mike Schatz is an Assistant Professor of Quantitative Biology at Cold Spring Harbor Laboratory. Prior to getting into the world of genomics and bioinformatics, Mike worked for a startup company that specialized in network security (working on encryption software for online banking amongst other things):

It was unplanned serendipity, but code breaking turned out to be perfect training for genomics, and the startup turned out to be perfect training to become a PI.

His research focuses on the development of scalable algorithms and systems to analyze biological sequence data, concentrating on the alignment, assembly, and analysis of high-throughput DNA sequencing reads. If you visit his lab research page, you will see an impressive list of software tools that he has helped develop.

Aside from his contributions to genomics, I am perhaps more impressed that Mike has made available slides from all of his major research presentations going back to 2005 (over 80 talks). I wish more scientists were as dedicated at sharing talks like this. You can find out more about Mike from his l ab website or by following him on twitter (@mike_schatz). And now, on to the 101 questions...

001. What's something that you enjoy about current bioinformatics research?

What brought me into the field was the opportunity to apply my training and experience in computer science to really meaningful problems in biology and medicine. I’m fascinated by the deep connections between how computers and software are organized and operate compared to how cells and genomes are replicated, transcribed, and evolve.

Right now is by far the most fantastic time to be in a field that is driven by rapid improvements to the biotechnology. How amazing that just 15 or 20 years ago it would have been cheaper and easier to land a team on the moon than to sequence their genomes, but now we do it on a routine basis!

This growth has fundamentally and forever changed the types of questions that we can even ask. The really exciting and scary point is we are still at the very beginning, and are still feeling around in the dark. I recently gave a talk about how long we should expect to wait until we have sequenced one billion genomes (hint: it is a lot sooner than you might expect).

010. What's something that you *don't* enjoy about current bioinformatics research?

The FASTQ “file format”. Do we really need the read identifier listed twice (sometimes), newlines within a single record, and an unspecified encoding scheme for quality values that changes every so often depending on when the software was run?

I cringe every time I have to teach it to a new student. There is no rational to it and it's so obviously flawed. It just feels dirty to teach it. I like to think that in 10 or 100 years this will all be sorted out, but today, this and so many other poorly designed systems are entrenched into our day-to-day lives. It is a constant, if dull, irritation that makes everything slow to change, and brittle to use.

011. If you could go back in time and visit yourself as an 18 year old, what single piece of advice would you give yourself to help your future bioinformatics career?

Take more probability and statistics. So much of my life now is spent looking for patterns in enormously large and complex data that the only hope is through statistical analysis. I used to stay up late reading algorithms textbooks, but now this is where I spend my free time.

The one really successful tip I’ve learned is that, even though my intuition for probability is poor, I can often work backwards using a simulator. I’ll write a little code so I can look at what happens to the distribution if this rate goes up, or if the genome was twice as complex. I then use that to guide me to the analytical form. I always understand an algorithm better if I implement it from scratch, and I think that this is an extension of that concept.

100. What's your all-time favorite piece of bioinformatics software, and why?

Do I have to pick just one? Ben Langmead blew my mind when he taught me about the FM-index. A very close second was the genome assembler Art Delcher wrote in about 50 lines of awk. More recently my lab went over the SGA algorithm from Simpson and Durbin in great detail. All of these have beauty in their simplicity and elegance — like a great work of art everything locks together perfectly in step.

101. IUPAC describes a set of 18 single-character nucleotide codes that can represent a DNA base: which one best reflects your personality?

S – It is the strongest code, of course! ;)