Time for a classic example of a JABBA-award winning piece of bioinformatics software

jabba logo.png

Normally, I introduce the name of the JABBA-award-worthy acronym before I show you the full name of the offending piece of software. But this time, let's play a little game. Here is the title of a recent article from the journal Bioinformatics, only I have removed the software acromym and the tell-tale capitalization from the name:

small molecule activity scanner web service based

So now you know the name, have a guess at what the acronym/initalism is. I feel confident that no-one will guess the answer. You'll have to scroll down for the reveal…

 

Okay, here it is:

SEABED: Small molEcule activity scanner weB servicE baseD

Note that:

  1. Only the 'S' is clearly derived from the initial letter of a word
  2. The 'A' is left ambiguously unexplained in the capitalization (as presented in the journal title). One might presume that it comes from 'Activity' but I wouldn't rule out 'scAnner'.
  3. However you derive the letters in SEABED, one (or more) words don't contribute to the acronym at all.

All of which makes SEABED a worthy recipient of a JABBA award. The only saving grace is that a Google search for seabed bioinformatics finds the paper as the top hit.

One downside to this tool is that the SEABED webserver (http://www.bsc.es/SEABED) doesn't seem to working at all at the moment.

Tales of drafty genomes: part 2 — when draft genomes took over the world

This is the second post in an infrequent series that looks at draft genomes.

At the time of writing, Google has indexed almost 400,000 pages that include a mention of the phrase draft genome. Prior to the year 2000, there are zero mentions of this phrase in the tech giant’s search index.

The phrase ‘draft genome’ came to prominence with the publication of the ‘working draft’ version of the human genome[1]. But referring to published genomes as anything other than ‘complete’ was still atypical at this time. This can be seen if you search Google Scholar for papers that include in their titles either the phrase draft genome sequence or complete genome sequence. When you look at how these results change over time, an interesting pattern emerges:

Number of papers indexed by Google Scholar that include the phrases 'Complete genome sequence' or 'Draft genome sequence' in their titles.

Around 2000–2003, there were a small number of papers mentioning draft genome sequences. These are nearly all related to the draft sequences of the human or rice genomes. Usage of the phrase (in journal titles) didn’t break double digits until 2011. Draft genomes then became a much more widely used phrase in 2012 and by 2013 they overtook usage of ‘complete genome sequence’

I find this reveals something about the nature of sequencing and genome assembly. It almost feels like we are giving up our ambition to finish genomes (whatever ‘finished’ actually means) and are more willing to settle for something that is clearly incomplete.

A definition of ‘draft’ provided by Merriam-Webster is as follows:

A version of something (such as a document) that you make before you make the final version

In an ideal world, I would hope that all of these draft genomes would also end up being replaced by ‘final versions’. But I’m doubtful that many of these published sequences will be completed any time soon.

  1. See part 1 in this series for more details about the drafty nature of the human genome.  ↩

Sowing the seeds of bad bioinformatics names

Here are two simple pieces of advice for people who are looking for a name for their latest bioinformatics tool/database/resource:

  1. Avoid common words which might cause people searching for your tool to find something else instead.
  2. Choose a name that hasn't been used before by the bioinformatics community.

Having said that, let's look at a new paper in the journal Bioinformatics:

Seed: a user-friendly tool for exploring and visualizing microbial community data

This name 'Seed', is a not-too-offensive acronym for Simple Exploration of Ecological Data. So what's my beef with it?

The problem is that words like seed are going to appear all over the Internet. My standard test for the 'searchability' of a bioinformatics tool is to search for the tool name followed by the word 'bioinformatics'. Your resource's website or publication should hopefully be the number one result (or somewhere on the first page). However, that is not what happens here.

And searching for 'seed bioinformatics' raises more problems by clashing with my first piece of advice. E.g. here are a couple of papers that were in my first page of Google results:

2010: Accessing the SEED Genome Databases via Web Services API: Tools for Programmers

2011: SEED: efficient clustering of next-generation sequences

So what happens if you include 'microbial' into your search terms? Won't that help?

Nope. Turns out that the SEED — not an ancronym as far as I can tell — is an annotation environment for microbial genomes that has been around for a decade, and which has spawned many papers, e.g.:

2014: The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

All of which means that people looking to find the newly published Seed tool, are not going to have much luck when using search engines.

Is it a 'bad idea' to include gratuitous pictures of cleavage on an Oxford Journals website?

In a word, 'yes'.

2015-02-16: Note that this story has been updated after Oxford Journals contacted me about this (see end of post).

I know that journals need to make money, but it seems a bit shoddy when they allow any form of advertising to appear on their websites. Came across an article at Nucleic Acids Research today which featured the following advert:

Given that I have published in this journal before, I suppose that people reading our articles will also have a chance of seeing ads like this. I would ask Oxford Journals to think carefully about whether they really want adverts like this appearing on their site. This doesn't seem a particularly good fit for them as a scientific publisher — for that matter, it doesn't seem a great fit for for the advertiser either.

Update: Oxford Journals reached out to me on twitter with some good news:

BLAST bug (or feature?) in NCBI BLAST v2.2.30+

Something changed in the latest version of NCBI BLAST+ which breaks our CEGMA software. Compare the behavior of this simple TBLASTN command in v2.2.29+ and v2.2.30+ (from October 2014):

v2.2.29+

tblastn -db sample.dna -query sequence.prot -word_size 5

TBLASTN 2.2.29+

Database: sample.dna
           1 sequences; 2,499,950 total letters

Query= 7292122___KOG0292

Length=1234
                                                 Score     E
Sequences producing significant alignments:      (Bits)  Value

CHROMOSOME_I 1 15072418                           38.9    0.002

v2.2.30+

tblastn -db sample.dna -query sequence.aa -word_size 5

BLAST query/options error: Compressed alphabet lookup table requires word size 6 or 7
Please refer to the BLAST+ user manual.

One step in the CEGMA pipeline involves running TBLASTN with a word size of 5. This no longer works in the latest version and the error message suggests that only a word size of 6 or 7 is permitted. I can confirm that this is the case by looking at the latest source code for the blast_option.c file:


else if (options->lut_type == eCompressedAaLookupTable &&
         options->word_size != 6 && options->word_size != 7) {
         Blast_MessageWrite(blast_msg, eBlastSevError, kBlastMessageNoContext,
               "Compressed alphabet lookup table requires "
               "word size 6 or 7");
         return BLASTERR_OPTION_VALUE_INVALID;
}

The error message suggests I look at the BLAST+ user manual. I did this, and according to Table C5:

tblastn application options:

option = word_size    
type = integer
default value  = 3 
description and notes = "Valid word sizes are 2-7."

There also seems to be no mention of this change in the release notes, all of which makes me think that this is a bug. So I will report this to the NCBI, but any CEGMA users out there may wish to hold off updating to v.2.2.30+.

10 bioinformatics tools you should be using on Valentines Day

1. HUGS: the database of HUman Genome Sequences

"We envisage that the growth in personal genomics will mean that researchers will increasingly want HUGS to cope with their work."


2. LOVE: LncRNA Ortholog Validation and Evaluation

"If you are unsure as to the quality of your lncRNA annotations, we suggest that you need LOVE."


3. KISSES: Kmers In aSsembled SEquenceS 

"We envisage that KISSES will be widely distributed by people working in the field of genome assembly."


4. HEART: Histidine Enrichment Analysis Report Tool

"Accurate detection of histidine-enriched sequences can be achieved if researchers have HEART."


5. ILOVEYOU: Intergenic LOng VariablE Yeast Operational Units

"Detection of this new class of conserved intergenic element will open new avenues for S. cerevisae researchers, and we predict that many will benefit from a deeper understanding of ILOVEYOUs".


6. ROSESARERED: Random Ortholog SEquence Simulations that ARE REDundant

"This tool effectively generates a series of, largely pointless, simulated ortholog sequences. See also our companion software: ValIdation Of Long Eukaryotic TranscriptS thAt Randomly appEar BioLogically UsEful (VIOLETSAREBLUE)."


7. VALENTINE: VALidation of ENcode Transcriptomes IN Eukaryotes

"We believe that the ENCODE annotations of the human genome are only 80% useful, therefore genome annotators will likely appreciate a VALENTINE."

 

8. PASSI(ON): Predicting ASSembly Integrity (Or Not)

"Based on our observations, we feel that there is an urgent need for PASSI(ON) within the genomics community."

 

9. CHOCOLATES: CHOosing COmputationaL AlgoriThms for Testing Evolutionary Simulations

"In a field where which increasingly offers a bewildering choice of bioinformatics tools, we feel that researchers will appreciate CHOCOLATES."


10. SNUGGLES:: SearchiNg for Unique Genes in orGanisms Like Eels and Snakes

"There is a desperate shortage of bioinformatics tools that are dedicated to finding unique genes in creatures that look a bit like worms. Hence we are confident that the community of people who work on snakes, eels, nematodes, and other tubular-like organisms will be receptive to SNUGGLES."

 

 

 

Can you say the name of this new bioinformatics method three times fast?

New in the journal Bioinformatics:

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data

JNMF stand for Joint Non-negative Matrix Factorization. Throw in some meta-analysis and randomly decide to make the 'J' lower-case as well as itacilized and you end up with the trips-off-the-tongue name of jNMFMA. Try saying it three-times fast! Actually, I had trouble pronouncing this just once.

How not to write a sequence assembly comparison paper →

A great post by Keith Robison in which he casts his expert eye in the direction of a new pre-print published at F1000 Research.

In the preprint there is a a very ill-designed figure, which should be a table, that prints badly, with the font much too light for the unnecessarily heavy background. Displaying a four quadrant image adds nothing; a neatly organized table could display more information in a far more readable format allowing easy comparison. Even if the design were defensible, the content is embarassingly out-of-date, I'd estimate by close to two years.

The post ends with a great coda.

If cars were made by bioinformaticians... →

Saw this on Twitter today:

Guillaume has some fun with this topic on his blog (The Grand Locus). Obviously I liked the first item on the list the most ('Cars would have nice names'), this included:

Here we present CaЯ (vehiCle for chAnging geo-cooЯdinates), a fast and accurate tool as an alternative to existing vehicles.

Genome Assembly: the art of trying to make one BIG thing from millions of very small things

Here are the slides from a talk I gave this week at UC Davis (also embedded below). This talk was for a group of graduate students (from different backgrounds). 

Note, because I tend to make very visual slides which don't always work well in isolation (you need to hear my sparkling narrative!), I have taken time to duplicate many slides and embed notes to indicate approximately what I would have said to explain the slide.