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 their bioinformatics careers.
His blog, Getting Genetics Done, should be required reading for anyone who wishes to get lots of practical, hands-on, advice about doing bioinformatics. This is especially so if you want to know more about R (he has 140 posts on the topic!). He has a great overview about the goal of the blog:
Many resources offer a 10,000-foot view of the current trends in the field, reviews of various technologies, and guidelines on how to effectively design, analyze, and interpret experiments in human genetics and bioinformatics research. By comparison very few resources focus on the mundane, yet critical know-how for those on the ground actually doing the science (i.e. grad students, postdocs, analysts, and junior faculty). Getting Genetics Done aims to fill that gap by featuring software, code snippets, literature of interest, workflow philosophy, and anything else that can boost productivity and simplify getting things done in human genetics research.
001. What's something that you enjoy about current bioinformatics research?
I'm faculty in Public Health but my primary position is directing our Bioinformatics Core. That means I get to work on all kinds of projects with a very diverse set of collaborators. Monday I might be assembling plant genomes for a collaborator in the biology department, Tuesday I might be working on RNA-seq in patient kidney biopsies with a urologist in the hospital, the next day I might be figuring out how to best approach hybrid assembly with Nanopore and short read sequencing for a plasmid genome. Every day is something different, and the job never gets boring.
010. What's something that you don't enjoy about current bioinformatics research?
Same answer as 001: working on all kinds of projects with a very diverse set of collaborators.
Seriously, as fun as this can be, I often have to sacrifice depth of expertise for breadth. And I think most other bioinformaticians who exist for collaboration have to do the same. I have to be an expert in data analysis and study design of hundreds of different *-seq assays. I can't spend two months working on hybrid assembly with Nanopore and short read sequencing for one collaborator when I have a PAR-CLIP project, an exome variant-calling/annotation project, a 16S microbial profiling project, and a breakpoint mapping project with other collaborators, all needing the same level of attention to detail.
011. If you could go back in time and visit yourself as a 18 year old, what single piece of advice would you give yourself to help your future bioinformatics career?
Take some programming classes in college, and try contributing to an open-source project.
I, like many other bioinformaticians, am a self-taught programmer. I cut my teeth on Perl years ago before Python was so popular, and have picked up a handful of other generic programming languages and numerical/statistical computing languages since then. But I'm not a software engineer, and at this point I'll only be able to polish my software development practices so much. Sure, most of my code is version controlled, and I know very well how to modularize code with functions, but there's much more to writing and contributing to good software than this. Good science increasingly relies on great software, and not just in genomics. More formal training would have been nice to have.
100. What's your all-time favorite piece of bioinformatics software, and why?
It's not one piece of software, but the Bioconductor community in general is just awesome. Pick any of the applications I mentioned in questions 001 and 010, and there's probably a Bioconductor package to help you with it. Most packages have great documentation, and reliance on a common set of data structures really simplifies things. The mailing list is responsive, and you don't have to have the same thick skin necessary to email R-help.
If I had to nail it down to just one single application, I'm going to have to be unoriginal and go with BEDTools. Way back when, I used to load genomic intervals into MySQL database tables and write impossibly complex (and slow) queries to do very simple BEDTools-y kinds of operations. Just when you think you have a one-of-a-kind "genome arithmetic" problem that no one has ever seen before, you'll often find that you're not so special after all and there's a BEDTools subcommand or recipe that gets you exactly what you need.
101. IUPAC describes a set of 18 single-character nucleotide codes that can represent a DNA base: which one best reflects your personality, and why?
Besides knowing the ins and outs of many different kinds of NGS studies, what makes a bioinformatician a great scientist is being really good at lots of things at once: a skilled programmer, a skeptical statistician, an influential writer, a perceptive reader, a captivating speaker, a convincing salesman, a careful financial planner, a creative graphic designer, a thoughtful experimentalist, and a friendly colleague. I'm certainly not all of these things, but I'm still going to go with N.