THe popUlARiTY of VARioUS iUpAC NUCleoTiDe AMBiGUiTY CHARACTeRS

There have now been 18 interviews in my series of 101 questions with a bioinformatician. The final question in each interview is always:

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

So after 18 interviews we have the statistical possibility of equal representation by all possible nucleotide ambiguity codes. Let's take a look at what the results actually look like:

So N and Y are the most popular choices so far but no love for A, C, G, U, K, M, D,  or H! What's so bad about the letter K? I always thought of K as a distinguished member of the IUPAC ambiguity code community!

If you are sharp-eyed you may notice that there are actually 19 responses shown here. That's because a certain someone claimed two characters in their answer. I'm sure that you will all be glued to the next 18 interviews to see if, and how, these frequencies change. And I will be Keen in my undertaKing to maKe sure that I Keep this blog free from any subtle bias that may influence folK.

10 tips for improving your presentations & speeches →

Some fantastic advice here from the Presentation Zen site (which is always worth looking at). Many scientific presentations would be greatly enlivened if presenters took more effort to turn a collection of facts and observations into a story. Tip #4 is something that I frequently mention to students in our lab:

(4) Have a clear theme. 
What is your key message? What is it you REALLY want people to remember? What action do you want them to take? Details are important. Data and evidence and logical flow are important. But we must not lose sight of what is really important and what is not. Often, talks take people down a path of great detail and loads of information, most of which is completely forgotten (if it was ever understood in the first place) after the talk is finished. The more details that you include and the more complex your talk, the more you must be very clear on what it is you want your audience to hear, understand, and remember. If the audience only remembers one thing, what should it be? Write it down and stick it on the wall so it's never out of your sight. 

Sometimes students seem almost surprised by the notion that the audience should be expected to remember something from their talk.

Better Posters: A design brief for conference posters →

Zen Faulkes has a great post on the Better Posters blog, it's well worth a read if you think you will ever need to make an academic poster. This section nails it for me (emphasis mine):

Goals of a poster

Posters should get conference attendees talk to the presenter. Because attendees are busy, posters must grab attention, even if a potential reader is quite a long way from the poster. Similarly, posters should make an implicit promise to the reader that the gist of the poster can be grasped quickly.

Posters should also contain enough information that a person is able to read it and understand the main message.

Sprai: a bioinformatics software acronym that I actually like

I heard somebody mention the following tool today:

As someone who frequently heaps scorn on many bogus acronyms used for bioinformatics tools, I thought that maybe I should point out some good ones when I come across them. I like 'sprai' as a name for the following reasons:

  1. It is a genuine acronym (initial letters of all words are used)
  2. The name is short
  3. The name is pronounceable (though I'm making an assumption that it is pronounced 'spry')
  4. As a non-English word, it's pretty unique (especially in the field of bioinformatics)
  5. When I search for it on Google, it shows up on the first page of results (though it has some competition from this place)

The only negative might be that it is subject to being misspelt (especially if you only hear the name verbally and don't see it written down), but otherwise it's a good name.

101 questions with a bioinformatician #16: Melissa Wilson Sayres

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.

Melissa Wilson Sayres is Assistant Professor of Genomics, Evolution, and Bioinformatics in the School of Life Sciences and The Biodesign Institute at Arizona State University. Her lab is interested in the evolution of sex chromosomes among other topics that relate to genome evolution and comparative genomics.

I applaud Melissa for clearly setting out both her expectations of people that join her lab in addition to listing her responsibilities to her lab members. I wish more PIs were as communicative about this, though I would add an expectation for grad students: 'I will not leave food — especially cheese — on, in, or near my computer'.

You can find out more about Melissa by visiting her (well documented) lab page, checking out her mathbionerd blog, or by following her on twitter (@mwilsonsayres). And now, on to the 101 questions...

 

 

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

That we can use computers to collect, analyze, and learn new things about our biology and evolutionary history.

 

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

That there isn't a straightforward way to get into it. Some people come from computer science and may feel intimidated about learning the biology. Some come from biology and are intimidated by learning to program. Some (like myself) come from some other background, and learn both! Although there are a few collegiate bioinformatics programs, it is my impression that many schools do not have the kinds of background courses that students need in order to break into bioinformatics. Many of us are self-taught. 

 

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?

Learn a programming language. 

 

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

I really like Galaxy, because it has the GUI-based format for newbies, as well as the command-line option for those who prefer it, and it makes computational biology easier to reproduce.

 

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

Y! Because the sex chromosomes are the most interesting (and there is no 'X' nucleotide ambiguity code).