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, HAMSTeRS, Pigeons, 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.

How to make your genomics website more suitable for an English-speaking audience

Today I visited the website of the Beijing Institute of Genomics (BIG) for the first time. BIG is not to be confused with BGI (which was formerly known as the Beijing Genomics Institute). If you look at just about any web page on this site other than the home page (which contains an unusual visual element), you'll see the following image:

My sharp, British-born, eyes quickly recognized this as the UK's Houses of Parliament in London (well technically it's the Palace of Westminster). See this image for a comparison. I then noticed that this image doesn't feature on the Chinese language version of the website (which has a completely different design).

I can only assume that some web designer thought that an image like this would be fitting because it is the English-language version of the website, and that they therefore chose an image of something (incorrectly) deemed to be English. At this point, I feel obliged to share the following video which offers a definitive explanation as to the differences between England, Great Britain, and the United Kingdom:

Reflections on my '101 questions with a bioinformatician' series

This is in lieu of a regular '101 questions with a bioinformatician' post which has been delayed (hopefully by only a day). This series of interviews has now been running for over 2 months and — judging by my web stats — it seems to be popular. In fact, these posts now account for the majority of traffic to this site.

Thanks to everyone who has contributed so far, and for everyone who has been reading these interviews. It's been fun doing this and I've enjoyed seeing the variety of answers that people have provided.

I should confess that I'm solely responsible for adding hyperlinks to the answers that people provide, and in addition to adding links for obvious items like pieces of bioinformatics software, I sometimes like to have a bit of fun with what I choose to link to. E.g. see the links I added to question 101 in my interview with Holly Bik.

To finish off, here are some relevant numbers about this series:

  • 10 — number of interviews posted
  • 2 — number of interviews finished and (almost) ready to be posted
  • 6 — number of people who have agreed to be interviewed but haven't yet sent me their answers (cough, cough).
  • 81 — my current list of 'potential interviewees'

The last point means that hopefully I can keep this series going for a while longer. I guess that I now have to aim for an interviewee #101, (which would be the 102nd interview…obviously).

Still collecting results for my survey about gender bias in bioinformatics

A quick post just to say that although I published some preliminary results from my survey about gender bias in bioinformatics, I left the survey live so that others could still add their responses. So far, I've had 28 more responses on top of the original 370. 

I also tweaked the survey form to allow ex-bioinformaticians to respond (and I asked whether they left bioinformatics as a career because of gender bias). If you haven't done so, please complete the form (embedded below) or available here. I'll try to update the main results on Figshare in a few weeks. Hopefully, with some more results it will be possible to see if there are other notable patterns in the results.

101 questions with a bioinformatician #9: Tuuli Lappalainen

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.

Tuuli Lappalainen is a Group leader at the New York Genome Center, an institution that's so new, that their Illumina HiSeq X Ten is counted as one of their older sequencing machines. In addition to having possibly the coolest logo for a genomics/bioinformatics institute, they also have an impressive set of green credentials. And did I mention that it's in New York, New York? Start spreading the newwwss…

Sorry, I got distracted.

Tuuli is also an assistant professor at the Department of Systems Biology at Columbia University. Her work focuses on using high-throughput sequencing data to study functional genetic variation in human populations. Her website — paraphrasing Dobzhansky — puts it like this:

Nothing in the genome makes sense except in the light of the transcriptome

You can find out more about Tuuli by following her on twitter (@tuuliel) or by checking out her lab's website. Oh, and Tuuli is looking for a talented post-doc to join her lab (she didn't ask me to say that, it's all part of the service). And now, on to the 101 questions...

 

 

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

I have very little interest in methods for the sake of methods; for me it's all about understanding biology, and bioinformatics provides fantastic opportunities for that.

 

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

The working environment that is local when data and analyses are increasingly global is driving me insane. I've done (and still do) a lot of consortium work, where all of us still end up copying large data files to our local servers, and having locally optimized pipelines and scripts that are impossible to transfer to colleagues. I know that many people are trying to solve the problem, and I hope we'll be able to make it happen soon. And then there are the complications of applying and getting access to various datasets. Privacy concerns are important, but does dbGap really need to be so difficult to use? Our open access data set from GEUVADIS (Genetic European Variation in Health and Disease) is a great exception to this.

 

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 more stats, math, proper programming. It's great to see how the younger generations have formal training in so many of the skills that I've had to just pick up the along the way — I'm a biologist by training and proud of it, but in the early 2000's computational biology was still very marginal. 

 

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

My two current favorites are pysam for handling BAM/SAM files — fast, great syntax, and much more versatile than alternatives — and Matrix eQTL for very fast eQTL analysis.

 

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

T for Tuuli!

Is the naming of bioinformatics software getting out of CoNtRol?

There is a new paper in the journal Bioinformatics. This is the title of that paper:

CoNtRol: an open source framework for the analysis of chemical reaction networks

Now people will know that I have no stomach for bogus bioinformatics acronyms and initialisms, so is CoNtRol worthy of a JABBA award? Well I can't give it such an award because CoNtRol is not an acronym or an initialism. At least I don't think it is. 

The abstract describes CoNtRol as a web-based framework for analysis of chemical reaction networks (CRNs). So even though the capitalized letters in CoNtRol give you CNR, maybe it's really all about CRNs???

The CoNtRol website makes things a little more confusing by starting their introduction with the text: CoNtRol (CRN tool) is a web application. Are you now thinking what I'm thinking? Is CoNtRol the world's first bioinformatics software based on an anagram (CoNtRol = CRN tool)? If this isn't the reason, then I can only assume that someone decided to just randomly capitalize various letters in the name.

Whatever the reason for the name, the more practical issue is that these tools can often be hard to find with web search engines. It doesn't show up on the first page of Google results if you search for control bioinformatics web app. Nor does it show up if you search for control chemical network app. There is something to be said for giving software novel names.

 

101 questions with a bioinformatician #8: Nick Loman

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.

Nick Loman is an Independent Research Fellow in the Institute of Microbiology and Infection at the University of Birmingham, UK. You may know Nick for his involvement in producing the only world map of high-throughput sequencers (at least I'm assuming that this is the only map of its kind…I'm too lazy to check). Maybe you know him for the exclusive interview that he managed to secure with some of Oxford Nanopore's head honchos at the 2012 AGBT meeting (the scene of a certain wowser moment in high-throughput sequencing). Or maybe you just know Nick for his epicurean passions.

I like to think of Nick as the Jack of Clubs in the deck of cards that is the bioinformatics blogging community (this works as a metaphor, right?). Actually, on some days he's more like the Ten of Diamonds, but then he goes and writes great pieces like this (co-authored with fellow 101 alumni Mick Watson):

If you are interested in bioinformatics, and if you want to keep up with the latest developments in high-throughput sequencing technology, then you really should be keeping a close eye on people like Nick (though not too close, give the man some privacy!).

You can find out more about Nick by following him on twitter (@pathogenomenick) or keeping up with his excellent blog (Pathogens: Genes and Genomes). And now, on to the 101 questions...

 

 

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

I mainly enjoy the daily battles with crashing servers with cryptic memory errors, incompatible software versions, buggy scripts (mine and others) and full hard drives. 

Hah! That was the famous British sarcasm you will have read about.

The obvious answer is that the projects I get involved in are incredibly diverse, and I get to interact with many interesting people, because sequencing and bioinformatics skills are in such demand.

Another thing I enjoy is that I can reach out, via Twitter and blogging, to discuss with all the great computation biologists in the world struggling with the same problems. I have no idea what it must be like to feel isolated and slog away in a windowless laboratory without that kind of communication.

 

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

I whinge quite a lot on my Twitter feed, but I wish bioinformaticians (including myself) wouldn't spend so much time reinventing the wheel (Keith: it's bioinformatics sin number 1 on this list), and instead try and muck-in together to solve really important problems.

A model of bioinformatics research a bit more like the Linux kernel might work. Imagine an international network of committed bioinformaticians working together. We would achieve great things quickly. But the academic model of recognition is broken for things like this, where everyone needs their own papers to justify their positions.

 

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 guess I would have got into the details of Bayesian statistics and machine learning earlier. These skills are very useful and I am only picking them up properly just now (I am on a Medical Research Council Training Fellowship).

Probably would have slipped myself a copy of Grays Sports Almanac too.

More prosaic: GNU parallel I discovered way too late and is an essential tool. And screen.

 

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

There's very little you can't get done with BLAST. It has its funny little quirks, but you know where you are with it. 

 

 

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

Well, it would be rather British to suggest T. But I prefer coffee.