This JABBA-award-winning bioinformatics tool should be 'detonated'

Another week, another JABBA award for Just Another Bogus Bioinformatics Acronym. The title of the paper — published on bioRxiv.org — that describes this tool, does not reveal the name:

Neither does the abstract, but when you get to the end of the Introduction, it is finally revealed:

We improve upon the state-of-the-art in transcriptome assembly evaluation by presenting DETONATE: DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation 

Wow. Three of the eight letters in this name do not come from the initial letters of words, and five out of eleven words in the full name of the tool do not contribute to the acronym at all. I particularly like the 'with or without' part.

While I can understand why they didn't want to use the full acronym (DNTRAWOWTTE), I'm sure that they could have come up with something else instead  — how about TETRA: Truth Evaluation Transcriptome RNAseq Assembler? But really, this is yet another example where you don't need to make an acronym! Just call the tool 'Detonate' and be done with it.

MIRA, MIRA on the wall: the problem of duplicated names in bioinformatics

So in addition to lots of bioinformatics tools that use bogus acronyms for their names, or which have very unpronounceable names, we now have a new problem…duplicate names. Rachel Glover (@rach_glover) tweeted this today:

The new MIRA tool (Mutual Information-based Reporter Algorithm for metabolic networks) is entirely unrelated to the existing MIRA tool which is a genome assembler that's been around for over 15 years.

It is not uncommon to need to search online for a bioinformatics tool. This can be complicated by the fact that many tools have names that are more commonly associated with other things (e.g. SHRiMP, ICEberg, HAMSTeRSPigeons, MOUSE, INSECT etc.). The first three examples also highlight that using mixed capitalization to help distinguish your bioinformatics tool from other things doesn't really help when you use a web search engine. 

One solution to this problem has always been to add the word 'bioinformatics' to your web search. However, if we start seeing more tools that share the same name, then this might not be that useful either.

Following Rachel's tweet, Torsten Seemann (@torstenseemann) had a suggestion:

I can't imagine that this would be an easy undertaking, but Alastair Kerr (@alastair_kerr) made a good follow-up point:

I think this is a great suggestion. Bioinformatics journals should perhaps state in their author guidelines that people should not duplicate the name of an existing (published) bioinformatics tool. Reviewer guidelines could also prompt the reviewer to check if this has happened (a simple web search of '<tool name> bioinformatics|genomics' would probably suffice).




Unpronounceable — why can't people give bioinformatics tools sensible names?

Okay, so many of you know that I have a bit of an issue with bioinformatics tools with names that are formed from very tenuous acronyms or initialisms. I've handed out many JABBA awards for cases of 'Just Another Bogus Bioinformatics Acronym'. But now there is another blight on the landscape of bioinformatics nomenclature…that of unpronounceable names.

If you develop bioinformatics tools, you would hopefully want to promote those tools to others. This could be in a formal publication, or at a conference presentation, or even over a cup of coffee with a colleague. In all of these situations, you would hope that the name of your bioinformatics tool should be memorable. One way of making it memorable is to make it pronounceable. Surely, that's not asking that much? And yet…

There is a lot of bioinformatics software in this world. If you choose to add to this ever growing software catalog, then it will be in your interest to make your software easy to discover and easy to promote. For your own sake, and for the sake of any potential users of your software, I strongly urge you to ask yourself the following five questions:

  1. Is the name memorable?
  2. Does the name have one obvious pronunciation?
  3. Could I easily spell the name out to a journalist over the phone?
  4. Is the name of my database tool free from any needless mixed capitalization?
  5. Have I considered whether my software name is based on such a tenuous acronym or intialism that it will probably end up receiving a JABBA award?

101 questions with a bioinformatician #10: Lex Nederbragt

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.

This is the third 'binary' post in this series — where the interviewee number consists of just ones and/or zeros. If this fact makes you excited, then you probably need to get out more.


Lex Nederbragt works as a Bioinformatician at the Norwegian Sequencing Centre (where they probably do more than just sequence Norwegians). He is also an Associate Professor at the Centre for Ecological and Evolutionary Synthesis (CEES), University of Oslo.

As a Dutchman living in the least populous of the three Scandinavian Kingdoms, Lex can take comfort in knowing that the Netherlands retain the upper hand in their battles with Norway on the football field.

Away from football  — and this is the last chance you'll have to get away from football for the next few weeks — Lex is someone who posts fantastic amounts of useful information on his blog. If you have any interest in high-throughput sequencing and assembly, then you owe it to yourself to follow his blog updates. 

You can find out more about Lex by following him on twitter (@lexnederbragt), or reading his aforementioned blog (In between lines of code) or his other blog…presumably the world's only blog devoted to the Newbler assembler.

And so on to the 101 questions...

 

 

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

The increasing focus on reproducibility and reusability. Making sure others can reproduce your work is such a fundamental aspect of science, and computational work should be easy to reproduce in principle. It is fascinating to see how difficult this turns out to be in practice — even in cases where the description of the work is very complete.

 

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

I'm not the first one to complain about the seemingly unlimited growth in tools meant for the same job, e.g., short read mappers. My field of interest is de novo genome assembly, and there too new tools appear regularly. I think it is about time we settle on a set of tools that appear to be best suited for the job, and move on to finding ways to determine which tools works best for each individual dataset and research question. In the case of assembly, we basically already know the set of programs that generally perform well. Now we need to develop and implement evaluation tools that tell a researcher which assembly of the data is the best one for their purposes.

 

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?

I am a bit ambivalent here. It took me a long time to realize that I wanted to become a bioinformatician, I missed a lot of signals how much I enjoyed programming, for example. So, I would like to tell myself to explore computational science much more than I did. On the other hand, waiting this long to make the switch to bioinformatics meant I have acquired a very firm background in biology. I find this essential for my work, as it allows me to make connections between the technological aspects of high-throughput sequencing experiments and data analysis, and the biological questions that inspired the experiments in the first place. So, I would also like to tell myself to keep on studying biology.

 

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

The Newbler assembly and mapping program from Roche/454 Life Sciences. It is not the program per se (it's good, but not necessarily the best; nor is it open source, for that matter). However, it is through the use of this program I was propelled into bioinformatics. I became very familiar with it and started scripting to massage its output. I even wrote a user-oriented manual for Newbler. These days, I use many more assembly programs besides Newbler, but my bioinformatics 'roots' will always be Newbler.

 

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

B, as it stands for 'C or G or T', so it is flexible, allowing several alternatives and keeping options open. But it also means knowing your limits, not everything goes. I also like to have a 'plan B' in the back of my head.