JABBA, ORCA, and more bad bioinformatics acronyms

JABBA awards — Just Another Bogus Bioinformatics Acronym — are my attempt to poke a little bit of fun at the crazy (and often nonsensical) acronyms and initialisms that are sometimes used in bioinformatics and genomics. When I first started handing out these awards in June 2013, I didn't realize that I was not alone in drawing attention to these unusual epithets.

http://orcacronyms.blogspot.com

http://orcacronyms.blogspot.com

ORCA is the Organization for Really Contrived Acronyms a fun blog set up by an old colleague of mine, Richard Edwards. ORCA sets out to highlight strange acronyms across many different disciplines, whereas my JABBA awards focus on bioinformatics. Occasionally, there is some overlap, and so I will point you to the latest ORCA post which details a particularly strange initialism for a bioinformatics database:

ADAN - prediction of protein-protein interAction of moDular domAiNs

Be sure to read Richard's thoughts on this name, as well as checking out some of the other great ORCA posts, including one of my favorites (GARFIELD).

ACGT: a new home for my science-related blog posts

Over the last year I've increasingly found myself blogging about science — and about genomics and bioinformatics in particular — on my main website (keithbradnam.com). Increasingly this has led to a very disjointed blog portfolio: posts about my disdain for contrived bioinformatics acronyms would sit aside pictures of my bacon extravaganza.

No longer will this be the case. ACGT will the new home for all of my scientific contemplations. So what is ACGT all about? Maybe you are wondering Are Completed Genomes True? or maybe you are just on the lookout to see someone Assessing Computational Genomics Tools. If so, then ACGT may be a home for such things (as well as Arbitrary, Contrived, Genome Tittle-Tattle perhaps).

I've imported all of the relevant posts from my main blog (I'll leave the originals in place for now), and hopefully all of the links work. Please let me know if this is not the case. Now that I have a new home for my scientific musings —  particularly those relating to bioinformatics — I hope this will encourage me to write more. See you around!

Keith Bradnam

Paper review: anybody who works in bioinformatics and/or genomics should read this paper!

I rarely blog about specific papers but felt moved to write about a new paper by Jonathan Mudge, Adam Frankish, and Jennifer Harrow who work in the Vertebrate Annotation group at the Wellcome Trust Sanger Institute.

Their paper, now out in Genome Research, is titled: Functional transcriptomics in the post-ENCODE era.

They brilliantly, and comprehensively, list the various ways in which gene architecture — and by extension gene annotation — is incredibly complex and far from a solved problem. However, they also provide an exhaustive description of all the various experimental technologies that are starting to shine a lot more light on this, at times, dimly lit field of genomics.

In their summary, they state:

Modern genomics (and indeed medicine) demands to understand the entirety of the genome and transcriptome right now

I'd go so far as to say that many people in genomics assume that genomes and transcriptomes are already understood. I often feel that too many people enter this field with false beliefs that many genomes are complete and that we know about all of the genes in this genomes. Jonathan Mudge et al. start this paper by firmly pointing out that even the simple question of 'what is a gene?' is something that we are far from certain about.

Reading this paper, I was impressed by how comprehensively they have reviewed the relevant literature, pulling in numerous examples that indicate just how complex genes are, and which show that we need to move away from the very protein-centric world view that has dominated much of the history of this field.

LncRNAs, microRNAs, and piwi-interacting RNAs are three categories of RNA that you probably wouldn't find mentioned anywhere in text books from a decade ago, but which now — along with 'traditional' non-coding RNAs such as rRNAs, tRNAs, snoRNAs etc. — probably outnumber the number of protein-coding genes in the human genome. Many parts of this paper tackle the issue of transcriptional complexity, particularly trying to address the all-important question how much of this is functional?

I found that so many parts of this paper touched on previous, current, and possible future projects in our lab. Producing an accurate catalog of genes, understanding alternative splicing, examining the relationship between mRNA and protein abundances, looking for conservation of signals between species...these are all topics that are near and dear to people in our lab.

Even if you have no interest in the importance of gene annotation — and shame on you if that is how you feel — this paper also serves as a fantastic catalog of the latest experimental techniques that can be used to capture and study genes (e.g. CAGE, ribosome profiling, polyA-seq etc).

If you have ever worked with a set of genes from a well curated organism, spare a thought for the huge amount of work that goes into trying to provide those annotations and keep them up to date. I'll leave you with the last couple of sentences from the paper...please repeat this every morning as your new mantra:

Finally, no one knows what proportion of the transcriptome is functional at the present time; therefore, the appropriate scientific position to take is to be open-minded. We thus do not claim that the annotation of the human genome is close to completion. If anything, it seems as if the hard work is just beginning.

More JABBA awards for inventive bioinformatics acronyms

A quick set of new JABBA award recipients. Once again these are drawn from the journal Bioinformatics.

  1. NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data - the mixed capitalization of this software tool is a little uneasy on the eye. But more importantly, a Google search for 'netweavers' returns lots of links about something entirely different. I.e. NetWeavers (and NetWeaving) is already a recognized term in another field.
  2. GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. - the 3 part of this algorithm's name is deliberately written in superscript by the authors. This implies 'cubed', but I think it is really referring to 3 lots of 'M' related words because the full name of the algorithm is 'Gene Inactivation Moderated by Metabolism, Metabolomics and Expression'. GIM3E is not something that is particularly easy to say quickly, though it is much more Google friendly than NetWeavers.
  3. INSECT: IN-silico SEarch for Co-occurring Transcription factors - making an acronym into the name of a plant or animal name is quite common in bioinformatics. A couple of examples are worth mentioning. There is the MOUSE resource (Mitochondria and Other Useful SEquences) and also something called HAMSTeRS (the Haemophilus A Mutation, Structure, Test and Resource Site). The main problem with acronyms like these is that they can be to hard to find using online search tools (e.g. Google for hamster resources). A secondary issue is that the name just doesn't really connect to what the resource/database/algorithm is about. The INSECT database contains information about 14 different species, only one of which is an insect.
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I'll no doubt be posting again the next time I come across some more dubious acroynms.

Top twitter talent: UC Davis genome scientists lead the way

The Next Gen Seq website has just published its 2013 list of the Top N Genome Scientists to Follow on Twitter. Over 10% of this International list of scientists are all staff or Faculty here at UC Davis, which says a lot about the quality of genomics talent here on campus:

It is also worth mentioning that there are so many other people at UC Davis who work in genomics and bioinformatics and who use twitter to effectively communicate their research and engage with the community. E.g.

  • @dr_bik - Holly Bik (Postdoc in Jon Eisen's lab)
  • @ryneches - Russel Neches  (Grad student in Jon Eisen's lab)
  • @theladybeck - Kristen Beck (Grad student in Ian Korf's lab)
  • @sudogenes - Gina Turco (Grad student in Siobhan Brady's lab...and winner of best twitter account name)

Great to see UC Davis recognized like this.

 

Update

Updated at 9:09 am to reflect that Next Gen Seq have now added Vince Buffalo to the list (he was apparently meant to be on the list anyway).

Another winner of the JABBA award for horrible bioinformatics acronyms

It's time to hand out another JABBA (Just Another Bogus Bioinformatics Acronym) award. Joining the recent recipients is a tool described in the latest issue of the Bioinformatics journal.

I don't have any problem with the acronym itself, and this is not a tool which is randomly adding or removing letters from the full name to produce the acronym. So what is my problem? Well the tool — which calculates a score to assess the local quality of a protein structure — is called The Local Distance Difference Test. And the acronym? Oh, the acronym is just 'lDDT' with a lower-case 'L'.

Now, this might not be so bad if it were not for the fact that all fonts used by the Bioinformatics journal (HTML & PDF versions) as well as the author's own website make this 'L' look like the letter I or the number 1.

From the HTML

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From the PDF

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From the author's website

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I can't help but imagine that people will only ever read this as IDDT and not LDDT...which of course doesn't bode well if someone ends up Googling for this tool at a later date. Compare a search for LDDT (which finds the correct tool) vs a search for IDDT (which doesn't:

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Congratulations on being the recipient of another JABBA award!

What's in a name? Better vocabularies = better bioinformatics?

About 7:00 this morning I was somewhat relieved because my scheduled lab talk had been postponed (my boss was not around). But we were still having the lab meeting anyway.

About 8:00 this morning, I stumbled across this blog post by @biomickwatson on twitter. I really enjoyed the post and thought I would mention in in the lab meeting. Suddently though that prompted me to think about some other topics relating to Mick's blog post.

Before I knew it, I had made about 30 slides and ended up speaking for most of the lab meeting. I thought I'd add some notes and post the talk on SlideShare.

What's in a name? Better vocabularies = better bioinformatics?

from

Keith Bradnam

I get very frustrated by people who rely heavily on GO term analysis, without having a good understanding of what Gene Ontology terms are, or how they get assigned to database objects. There are too many published anayses which see an enrichment of a particular GO term as some reliable indicator that there is a difference in datasets X & Y. Do they ever check to see how these GO terms were assigned? No.

New recipient of the Just Another Bogus Bioinformatics Acronym (JABBA) award

It was only a few weeks ago that I gave out the last JABBA award. One of the winning recipients that time was a database — featuring excessive use of mixed-case characters — called 'mpMoRFsDB'.

Well it seems that if you work on 'MoRFs' (Molecular Recognition Features) then you must love coming up with fun acronyms. This week in BMC Bioinformatics we have another MoRFs related tool that is worthy of a JABBA award:

The oh-so-catchy 'MFSPSSMpred' (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor) is the kind of name that requires you to first sit down and take a deep breath before attempting to pronounce it. Just imagine having to tell someone about this tool:

"Hi Keith, can you recommend any bioinformatics tools for identifying MoRFs?"

"Why certainly, have you tried em-eff-ess-pee-ess-ess-em-pred?"

Congratulations MFSPSSMpred, you join the ranks of former JABBA winners.

Some free code editors for Macs (that work in a UC Davis computer lab)

Every year I help teach a course[1] to grad students that hopefully leaves them with an understanding of how to use Unix and Perl to solve bioinformatics-related problems. We use a Mac-based computer lab because all Macs come with Unix and Perl installed. Many of our students are new to programming and many are new to Macs. Because of this, and because they need to use a code editor to write their Perl scripts, we have previously pointed them towards Fraise. Despite its age [2], this relatively lightweight editor has proved fine for the last few years that we have taught this course.

This year, however, Fraise proved problematic. The computer lab has now upgraded to OS X 10.8 which provides extra safeguards about what apps can be run. This Gatekeeper technology has been set up to only allow ‘signed’ apps[3]. The version of Fraise that we were using required administrator privileges for it to be opened (not possible in this computer lab).

My first thought was to direct students to download and install TextWrangler. This is an excellent, powerful, and well maintained code editor for Macs. Most importantly, it is free and also a signed app. However, it does try to install a helper app which caused a persistent dialog window to keep popping up during the installation. Clicking ‘cancel’ worked…but only after about 20 attempts[4]. I like TextWrangler as an app, but prefer the cleaner look of Fraise. So today I set out to find code editors for Macs that:

  • were free
  • could be run on the Macs in our computer lab (i.e. had to be signed apps)
  • were relatively simple to use and/or were easy on the eye

Here is what I came up with. These are all apps that seem to be under current development (updated at some point in 2013):

AppSize in MB Free? Notes

Komodo Edit301.1YesBig because it is part of a larger IDE tool which is not free[5]

Sublime Text 227.3sort of[6]Gaining in popularity (a version 3 beta is also available)

TextMate 230.3YesWhile this is technically an ‘alpha’[7] release, it seems very stable.

TextWrangler19.2YesVery robust and venerable app. Free since 2005

Tincta 25.6YesSmall app, similar to Fraise in appearance

 

If I had to suggest one, it would probably be Sublime Text 2 (though I will encourage students to buy this if they like it). TextMate 2 is a good second choice, particularly because it is also a very clean looking app. Of course, at some point we need to tell students about the joys of real text editors such as vi, vim, and emacs…but of course this might lead to hostilities![8]

  1. This course material is available for free and became the basis for our much more expansive book on the same topic  ↩

  2. Fraise is itself a fork of Smultron which stopped development in 2009 but which reappeared as a paid app in the Mac App Store in 2011.  ↩

  3. Those apps that are approved by Apple, even if they are not in the Mac App Store.  ↩

  4. Seriously, it takes a lot of clicks to make this dialog box go away. It then produces more pop-up dialogs asking whether you want to register, or install command-line tools.  ↩

  5. Currently $332 for a single license  ↩

  6. This is a paid app, but can be used in trial mode indefinitely with occasional reminders.  ↩

  7. TextMate 2 has been in alpha status since 2009  ↩

  8. Editor wars should be avoided if possible  ↩