Life after JABBA: ORCA and SOBBA?

I have published many posts on this blog under the JABBA heading. This stands for ‘Just Another Bogus Bioinformatics Acronym’ and the point of the JABBA Awards was to recognise extremely tenuous acronyms used by people when creating bioinformatics software and services.

It all started in June 2013 with a bioinformatics tool called ‘BeAtMuSiC’ winning the inaugural award. This short name is derived from a highly selective choice of letters from the full name of the tool:

Prediction of Binding Affinity Changes upon MutationS

This was a particularly contrived name because the C (from ‘Changes’) ends up at the end of the short name.

I think I wrote about 40 posts in total that gave out JABBA awards (some posts covered multiple tools). Even though I pretty much stopped posting on this blog after I moved on from my career in science, I did add a few more JABBA awards, the last one of which was awarded in March 2020 for a new tool called CHEER which derived its name from hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning.

ORCA

Around the same time that I started giving out JABBA awards, an old colleague of mine from my PhD days (Richard Edwards) started giving out something called ORCA awards. This stands for the Organization for Really Contrived Acronyms. ORCA set out to highlight strange acronyms across many different disciplines (whereas my JABBA awards focused on bioinformatics).

These started in August 2013, peaked the following year (with 29 posts) and seemingly ended in 2015. However, I just noticed that Richard popped up again in 2024 to highlight a new tool by himself: SHARPCLAW: Synteny, Homology And Repeat Pre-curation for Chromosome-Level Assembly Workflows.

SOBBA?

This week I learned of another collection of JABBA-adjacent awards. These are on a tumblr blog called Silly, Outrageous, or Bad Bioinformatics Acronyms. I’m not sure whether this is meant to be acronymised to ‘SOBBA’ or not.

The intro text on this blog states:

Carrying on in the footsteps of JABBA (Just Another Bogus Bioinformatics Acronym) and ORCA (Organization of Really Contrived Acronyms), gone but not forgotten.

I feel honoured for the shout-out there. I'm not sure who was behind this blog, but it burned brightly for the time it was up. The first post was made in October 2017 and the last post is from December of the same year. In the space of just under three months, the author did post an impressive 33 times, however.

New JABBA

It struck me that Richard Edwards’ most recent ORCA post means that he has been able to claim the ‘most recent’ prize in the category of JABBA/ORCA/SOBBA posts.

So I decided to have a look in the latest (September 2026) issue of the journal Bioinformatics (volume 42, issue 9) to see just how easy it would be to find a new candidate for a JABBA award.

First things first. I looked at the table of contents to see which papers included the name of a tool or service where they might be using any sort of acronym or initialism. I then went through each paper to work out any of those names were worthy of a JABBA award.

JABBA?

There was one software tool that came close to getting a JABBA award:

  • COBRA: cell-type-specific orthogonal batch effect removal algorithm in single cell RNA-sequencing data

According to the paper, this stands for ‘Cell-type-specific Orthogonal Batch effect Removal Algorithm (COBRA)’. Apart from dropping an ‘e’ from ‘effect’, this is not too bad. However, the associated GitHub page uses a different definition: ‘COrrection of BAtch effect for single-cell RNA-seq data’. This would be worthy of a JABBA award (and it could also be included in my JABBA menagerie), but I’ll give them a pass as that’s not the version used in the paper.

This does raise another issue in bioinformatics naming…be consistent! I guess the name is consistent, but the explanation is not.

JABBA!

So maybe you have stuck with this post in the hope of seeing an actual bona fide new JABBA award. Fear not. Like buses, three came along at once:

  • hicream: A flexible framework to identify significantly different regions in Hi-C data - the paper itself doesn’t say what ‘hi cream’ means, but a look at the associated R project page revealed all: hicream: HIC diffeREntial Analysis Method — so yep, taking two letters from the middle of a word to make your acronym means that it’s definitely worthy of a JABBA award.

  • BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction - Another JABBA Award is born. They explain that the name comes from Brain transcriptome-reGularized Hypergraph framework for mulTimodal disorder prediction.

  • BLOBFISH: bipartite limited subnetworks from multiple observations using breadth-first search with constrained hops - and another JABBA award (as well as another entry for the JABBA menagerie). This name is fun, but it takes some liberties with how it chose its letters: Bipartite Limited Subnetworks from Multiple Observations using Breadth-First Search with Constrained Hops)

Anything good?

I know it is all too easy to be critical, so I’ll single out one tool that had a name that I liked:

  • SignifiKANTE: efficient P-value computation for gene regulatory networks - a fun acronym-free name and bravo to the authors for spelling out the meaning so clearly in the paper: ‘from “signifikante Kante,” German for “significant edge”’

Bioinformatics names in 2026

I’ve not really looked at the names of bioinformatics tools for quite a few years. It feels a little depressing that it is still so easy to find dubious names that are worthy of a JABBA award. Finding three in one recent issue of a bioinformatics journal suggests that it would be easy to find others.

So I guess I’ll end this post with the advice that I have posted many times before. You don’t have to name something as an acronym or initialism. You can just give your tool a name that sounds interesting and/or fun. It’s more important that people can pronounce the name of your tool and be able to find it from a search engine or AI tool, than it is to have a tenuous name that tries to create an acronym from seemingly random letters in the full name of the tool.

And with that, I will reclaim the crown of ‘most recent blog post about dubious bioinformatics software names’.

Double-click to edit...

Finally: whole genome sequences that are whole

I started my genomics career literally at a time when the first eukaryotic genome sequence was published (yeast, 1996). My PhD thesis was titled ‘The molecular evolution of eukaryotic genomes’ which focused on the aforementioned yeast (Saccharomyces cerevisiae) and the first multicellular, and first animal, genome sequence of everyone’s favourite nematode Caenorhabditis elegans (published 1998).

The first plant genome sequence (Arabidopsis thaliana) was finished as I was writing my PhD thesis (published November 2000) and that genome became the focus of my first post-PhD job (working on developing genomic resources for UK CropNet - link from archive.org).

We then entered a long period where published genome sequences were often described as ‘draft’ or ‘near complete’. I have written about this a lot over the years, e.g.

So I was delighted to see the news this week of a collection of ‘T2T’ (Telomere 2 Telomere) genome sequences that have been published in Cell by the Telomere to Telomere Consortium. You should all read the commentary piece by Adam Phillippy et al.: Filling the holes in whole genomes: A vision for personalized genomics from telomere to telomere.

Quoting from the introduction (emphasis theirs):

As first stated in the 1980s, the aspirational goal of the Human Genome Project was “to obtain the complete nucleotide sequence of the human genome”

Most genomes published since that goal was declared were objectively far from complete. They were certainly useful and empowering for biology, just rarely complete.

Quoting from the end of the above paper:

These advances are already revealing features of genome organization and regulation within the previously dark regions of the genome and will undoubtedly improve the accuracy of future predictive models, signaling a bright future in which we can move beyond sequencing whole genomes and toward understanding whole genomes.

Complete genome sequences are here. These are sequences that can now capture the intricacies of the diploid nature of genomes. Anyone today starting a PhD that wants to look at ‘The molecular evolution of eukaryotic genomes’ has a much tougher challenge than I did (when N=2) but hopefully, they will be able to tell a much richer, and complete, story than I was ever able to.

Congratulations to all involved in this project!

Bioinformatics and genomics resources on reddit

 
 

Although many people in this field turn to sites like SEQanswers and Biostars to get help with bioinformatics problems, there are a number of subreddits that are devoted to discussion of bioinformatics and genomics. Reddit isn't just a forum for asking questions though, and people also share lots of relevant links (to papers, resources, news etc.).

I first made this blog post in 2015, but I’ve decided to review and substantially update it by adding details of many subreddits that didn't exist back in 2015.

Here is a roundup of the state of bioinformatics/genomics subreddits in 2026, arranged in order of popularity:

1. bioinformatics

  • URL: https://www.reddit.com/r/bioinformatics/

  • Created: January 2008

  • Description: A subreddit to discuss the intersection of computers and biology. A subreddit dedicated to bioinformatics, computational genomics and systems biology.

  • Weekly visitors: 38K

  • Weekly contributions: 437

This is the most popular subreddit in this list (in terms of visitors and overall use). The posts are roughly split equally between sharing links of interest and asking questions.

The number of daily posts has essentially remained the same in 2026 compared to when I first wrote this post in 2015. In the past, people used to ask questions about how to get into bioinformatics as a career, but I note that there is now a whole new subreddit for that (more on that later).

There is a good set of rules for this subreddit to try to cut down on irrelevant posts.

2. bioinformatics careers

This didn’t exist when I first created this post and I guess a lot of the career-related questions were all being posted to r/bioinformatics so this seems a very worthy addition which is heavily used.

It’s a sign of the times that one of the rules for this subreddit is ‘No AI training job postings’.

3. genomics

  • URL: https://www.reddit.com/r/genomics/

  • Created: September 2008

  • Description: No current description, but back in 2015 it was ‘Genomics, genetics, DNA, health, and personalized medicine’

  • Weekly visitors: 1.4K

  • Weekly contributions: 6

A decade ago, I noted that this subreddit seemed to be declining in popularity. Well, it is still regularly used, but it’s quite low traffic. There are a lot of questions that get asked here.

4. learnbioinformatics

The intended role of this subreddit has obvious overlaps with the other subreddits, but there are a lot of relevant, on-topic posts. Note, there are no posted rules for this subreddit.

5. bioinformatics tools

A relatively new subreddit. I can see the logic of trying to keep software-related discussion on its own subreddit, but feels like most people will still be using r/bioinformatics for this.

There are no rules specified for this subreddit, which feels a missed opportunity.

6. genome

The last post was six years ago. This subreddit is deceased.

Other honourable mentions

There are a lot of other subreddits that touch on aspects of bioinformatics and genomics. Comment if you have any further suggestions.

  • r/DNA - 6.1K weekly visitors - DNA and genetic testing. The science of DNA. Not for paternity topics.

  • r/sequencing_com - 326 weekly visitors - for discussing Sequencing insights, tips, tricks, and features. Have a question regarding DNA testing, WGS or Sequencing? Ask it here!

  • r/ngs - 35 weekly visitors - Next Generation Sequencing describes modern technology to sequence DNA and RNA

  • r/genomicepidemiology - 6 weekly visitors - A forum for bioinformaticians, public health professionals, clinicians, policymakers, and students to discuss genomic epidemiology.

  • r/MicrobeGenome - 4 weekly visitors - This is a community for microbial genomics tutorials, tools, and discussion.

What’s in a (gene) name?

Screengrab from WormBase database showing exon and intron structure of a gene called PPA52350

Screengrab from WormBase database showing exon and intron structure of a gene called PPA52350.

A chance conversation this week gave me a reason to check in on everybody’s favourite model organism database for nematodes…WormBase. Over two decades ago I spent four years of my life as a project manager for the UK arm of WormBase. Based at the Sanger Institute near Cambridge, we partnered with three groups in the USA (CalTech, CSHL and WashU) to maintain and develop the database that was used by thousands of nematode researchers around the world.

At the heart of WormBase was genetic and genomic data for the model organism Caenorhabditis elegans. This was the first animal to have it’s genome mapped and then sequenced. More impressively, the work to accurately fill in every last gap of the genome continued for many years after the formal publication of the genome sequence.

The 1998 Science publication described just over 19,000 protein-coding genes and a few hundred non-coding RNA genes.

I joined WormBase in 2001 and within a year or so I was tasked with developing what would frequently be referred to as simply ‘the new gene model’. Prior to any genome sequencing, there were many genes in C. elegans that had been defined by over two decades of classical genetic mapping approaches.

These genes were named in a simple, but consistent, way with three letters and then a number (this would necessarily have to be expanded to four letters in later years). E.g. unc-10 was the 10th gene named in the unc gene family (referring to UNCoordinated movements in worms with that mutation).

Enter the world of gene and genome sequencing

As the worm genome project began, genes would gain new identifiers based on in silico gene predictions made against the sequences of the cosmids, fosmids and YACs that would comprise the genome. So the first gene prediction on cosmid clone ‘T10A3’ would be named T10A3.1, the next adjacent gene prediction would be 'T10A3.2' and so on. Alternative splicing further complicated this nomenclature but it was relatively easy to append ‘a’, ‘b’, ‘c’ etc for splice variants. Luckily (for WormBase) I think 25 splice variants were the most ever discovered (see egl-8 in WormBase).

These sequence-based identifiers would sometimes become confusing when a new gene would be identified between the .1 and .2 gene predictions within a cosmid, fosmid or YAC. This messed up the original co-linearity of gene identifier with genomic location. E.g., you might end up with genes, F46H5.3 and F46H5.4 flanking a newly discovered gene which would gain a number such as F46H5.12 (you would use the next available number for that cosmid/fosmid/YAC).

When two genes go to war

The situation just continued to get more confusing. Turns out that a lot of computer gene predictions were not always right. This might mean removing a gene, or (more commonly) merging two genes into one new gene structure (where the original genes might now become splice variants. So genes C55B6.4 and C55B6.5 might become C55B6.4a and C55B6.4b (I’m using some made up examples here, but I recall it was somewhat arbitrary as to what gene identifier of the two merged genes would survive and which would die).

As genes were originally predicted on each assembled cosmid/fosmid/YAC sequence, and as these sequences overlapped — a necessary aspect of how the genome project was completed — it was possible that the same gene might be predicted independently on two overlapping cosmids/fosmids/YACs (and hence initially gain two unrelated gene identifiers).

Then of course there was the frequent situation where a classically defined gene would be matched with it’s sequence-identified counterpart. Hence, from my earlier example the unc-10 gene could also be known as T10A3.1.

Both names needed to be searchable and take you to the same entry in the database. But even more confusingly, there would be many more names that might exist. This could be because two researchers had independently mapped/published the same gene at different times, or just because some people would give the gene their own name without first referring to the literature. Generally speaking, the worm community is very good at avoiding this sort of thing but it did occasionally happen.

So unc-10 is not only the same gene as T10A3.1 but it could also be referred to as rim-1 or CELE_T10A3.1. I love that the WormBase database has preserved so many ‘curatorial remarks’ left by WormBase staff (myself included) as we tried grappling with how to merge genes and deal with other anomalies. E.g. here is a remark about the twk-18 gene:

”There are two twk-18 loci. One is CGC-approved (C24A3.6) and one is CGC-unapproved, which became an other-name of unc-58(T06H11.1a/b)”

This reflects yet another level of complexity where two different researchers or groups had used the same gene name for different genes.

Enter ‘the new gene model’

So it became my job to work out how we could:

  1. Create a new systematic identifier for every gene (yes, this reminds of me a bit of that XKCD comic)
  2. Roll this out across all of our parter groups in a way that didn’t break anything

This was a challenging problem but we eventually got there and all genes gained a new stable identifier that acted (hopefully) as a way to bring some order to how we (and others) worked with genes. These identifiers were ultimately rolled out in 2004 and an important part of ‘the new gene model’ was to allow a better way of capturing future gene births, deaths, mergers, and splits.

WormBase was based on ACeDB (A C. elegans DataBase), a bespoke database that was originally created by Richard Durbin and Jean Thierry-Mieg for the express purpose of managing C. elegans genetic data (it would go on to be used for many other organisms).

In AceDB you can always see the underlying model for any object by switching to something called the ‘Tree Display’. Amazingly (to me anyway), you can still do this in WormBase to the present day. It is buried within the Tree Display of genes, that you can see the original version control information that we added when we first migrated everything to ‘the new gene model’:

Screengrab from Tree Display view of a gene. Text in columns starts with 'Version_change' and includes a date column, a person identifier (WBPerson1971) and then a break down of an 'Event' which explains the detail of how the first import

Screengrab from Tree Display view of a gene in WormBase showing the Version change information for a gene.

In this case ‘geneace’ was the local ACeDB database that we used at the Sanger Institute to store information regarding all of the classically mapped genes. I was (and perhaps still am?) ‘WBPerson1971'; this means I am forever indirectly associated with most of the initial gene set of C. elegans in WormBase! This now brings me to the fundamental point that I wanted to make with this very long blog post…

What was the format of the new gene identifier?

I remember wanting to borrow from the format that the Ensembl genome database had established. Gene identifiers should have a fixed width by using leading zeroes to pad out the identifier (this makes it much friendly to computer programs that have to process such data). I also wanted there to be a simple text prefix to the identifier (Ensembl has gene IDs such as ENSG00000139618: ENSG = ENSembl Gene).

I went with ‘WBGene’ for the prefix part which just left the question of how many digits to reserve for the gene space. At the time I was working on this I think the genome of a related nematode C. briggsae had been finished. As I recall, WormBase contained that data as well as some very limited data for a few other nematodes (including some classically mapped genes as well as many sequence derived genes). So I knew that WormBase would grow beyond the 20,000 or so genes there were at the time of the C. elegans genome publication.

I opted for eight digits in the identifier, allowing for 99,999,999 genes. At the time this did seem like overkill, but I would rather err on the side of caution than give future bioinformaticans major headaches, e.g. what if we had gone for a five-digit number and then it turns out that we needed to store over 100,000 genes?

All of this meant that our unc-10 gene from before could now also be known as WBGene00006750.

An end to WormBase and an end to ‘the new gene model’

This week I wanted to have a look to see what the highest gene identifier was that had been added to WormBase. In taking a look at the website, I was saddened to see that WormBase came to an end in July 2025 with the 298th release of the database.

Thankfully, most of the data will live on in the Alliance of Genome Resources which is a consortium of seven model organism databases. It’s not clear to me whether the alliance will end up with yet another tier of identifier that will span all of the species that are represented. I note that unc-10 in their database currently gains a slight tweak with an extra prefix of ‘WB:’ to become WB:WBGene00006750 (presumably because they have the extra challenge of needing to know which database an identifier came from).

I wonder whether the new gene model will live on in the Alliance of Genome Resources and whether those eight digits of reserved identifier space will continue to fill up. Given how easy (and relatively cheap) it has become to sequence genomes these days, someone might decide they want to sequence the genomes of all nematodes (there are about 25,000 described species, but there could be many more).

The last gene in WormBase

And so I can finally bring this post to a close with the reveal of the last gene identifier that made it into the last release of WormBase:

WBGene00311061 (also known as PPA52350) is a protein-coding gene from the nematode Pristionchus pacificus. It is the highest number gene that I could find and it reflects the fact that the gene count of WormBase has increased over 15-fold since my time there.

I bet this is due to a lot of other species being added, but also due to an explosion in RNA genes in C. elegans. I’m glad that my choice of gene identifier all those years ago has survived.

In writing this blog post, it’s been fun a lot of fun to take this trip down memory lane. In my research for this post, I came across a recent video (March 2026) by the Alliance of Genome Resources which explains much more about how worm and fly genes are named. Tim Schedl is the gene name curator for the WormBase data within the Alliance dataset who took over from Jonathan Hodgkin who I worked very closely with during my time at WormBase.

If there is are any lessons to be learned from all of this (especially to any young bioinformaticians out there), I would say:

  1. It’s hard to design databases for biological data. Biology is messy and will produce surprises that might only emerge years after you define a schema that thought would capture all possible edge cases (biology will then laugh at your schema)
  2. If you can, try to future proof things as much as possible
  3. Do not - under any circumstances - allow asterisks or question marks to be part of a valid gene name! There were originally some genes in the geneace database that included asterisks which caused all manner of problems in ACeDB as asterisks were also used as a wildcard search operator.

Bad bioinformatics software names revisited

I recently have been sorting through lots of old notes files, including many from my time as a genomics researcher at UC Davis. One note file I had was called ‘Strategies for naming bioinformatics software’ and I initially assumed it was one of the blog posts posted on this blog.

However, I couldn’t find it as an actual post and when I did a quick web search, I instead discovered this ‘Bioinformatics lab’ podcast from earlier this year:

I have been out of the field of genomics/bioinformatics for many years now and didn’t know about The Bioinformatics Lab podcast which describes itself as ‘ramblings on all things bioinformatics’.

The conversation between the hosts (Kevin Libuit and Andrew Page) is good and listening to it brought back lots of memories from the many things I’ve written about on this blog. At the end of the episode, Andrew concludes:

“It’s kind of hard. People should bit a bit of effort into it”

100% this! Naming software should definitely not be an afterthought. Andrew goes on:

“Before you do any development on anything, go and choose a really good name and make sure it doesn’t conflict with any trademarks or existing tools, you can Google it easily and it’s not offensive in any language.”

These are the types of things that I have written about extensively on this blog. If you are interested, perhaps start with

Then you can ready any one of the nearly forty posts I wrote which handed out ‘JABBA awards’ which stands for Just Another Bogus Bioinformatics Acronym.

This award series started all the way back in 2013 and the inaugural award went to a tool with the crazy capitalisation of 'BeAtMuSiC'.

There’s also a series of posts on duplicate names in bioinformatics where people haven’t checked whether their software name is stepping on someone else’s toes.

This includes a post about the audacious attempt to name a new piece of bioinformatics software BLAST. There is also a post about the five different tools that are all called ‘SNAP’.

Admittedly I’ve been out of the loop for so long there is the possibility of there being many more SNAPs out there now!

The moral of this blog post is that names are important and it is very easy to mess them up which could end up meaning that fewer people ever discover your tool in teh first place.

CEGMA is dying…just very, very slowly

This is my first post on this blog in almost three years and it is now almost nine years since I could legitimately call myself a genomics researcher or bioinformatician.

However, I feel that I need to 'come out of retirement' for one quick blog post on a topic that has spanned many others…CEGMA.

As I outlined in my last post on this blog, the CEGMA tool that I helped develop back in 2005 and which was first published in 2007, continues to be used.

This is despite many attempts to tell/remind people not to use it anymore! There are better tools out there (probably many that I'm not even aware of). Fundamentally, the weakness of using CEGMA is that is based on an identified set of orthologs that was published over two decades ago.

And yet, every week I receive Google Scholar alerts that tell me that someone else has cited the tool again. We (myself and Ian Korf) should perhaps take some of the blame for keeping the software available on the Korf Lab website (I wonder how many other bioinformatics tools from 2007 can still be downloaded and successfully run?).

CEGMA citations (2011-2024)

When I saw that citations had peaked in 2017 and when I saw better tools come along, I thought it would be only a couple of years until the death knell tolled for CEGMA. I was wrong. It is dying…just very, very slowly. There were 119 citations last year and there have been 88 so far this year.

Academics (including former academics) obviously love to see their work cited. It is good to know that you have built tools that were actively used. But please, stop using CEGMA now! Myself and the other co-authors no longer need the citations to justify our existence.

Come back to this blog in another three years when I will no doubt post yet another post about CEGMA ('For the love of all that is holy why won't you just curl up and die!').

New BUSCO vs (very old) CEGMA

If I’m only going to write one or two blog posts a year on this blog, then it makes sense to return to my recurring theme of don’t use CEGMA, use BUSCO!

In 2015 I was foolishly optimistic that the development of BUSCO would mean that people would stop using CEGMA — a tool that we started developing in 2005 and which used a set of orthologs published in 2003! — and that we would reach ‘peak-CEGMA’ citations that year.

That didn’t happen. At the end of 2017, I again asked the question have we reached peak-CEGMA? because we had seen ten consecutive years of increasing publications.

Well I’m happy to announce that 2017 did indeed see citations to our 2007 CEGMA paper finally peak:

CEGMA citations by year (from Google Scholar)

CEGMA citations by year (from Google Scholar)

Although we have definitely passed peak CEGMA, it still receives over a 100 citations a year and people really should be using tools like BUSCO instead.

This neatly leads me to mention that a recent publication in Molecular Biology and Evolution describes an update to BUSCO:

From the introduction:

With respect to v3, the last BUSCO version, v5, features: 1) a major upgrade of the underlying data sets in sync with OrthoDB v10; 2) an updated workflow for the assessment of prokaryotic and viral genomes using the gene predictor Prodigal (Hyatt et al. 2010); 3) an alternative workflow for the assessment of eukaryotic genomes using the gene predictor MetaEuk (Levy Karin et al. 2020); 4) a workflow to automatically select the most appropriate BUSCO data set, enabling the analysis of sequences of unknown origin; 5) an option to run batch analysis of multiple inputs to facilitate high-throughput assessments of large data sets and metagenomic bins; and 6) a major refactoring of the code, and maintenance of two distribution channels on Bioconda (Grüning et al. 2018) and Docker (Merkel 2014).

Please, please, please…don’t use CEGMA anymore! It is enjoying a well-earned retirement at the Sunnyvale Home for Senior Bioinformatics Tools.

Three cheers for JABBA awards

jabba logo.png

These days, I mostly think of this blog as a time capsule to my past life as a scientist. Every so often though, I’m tempted out of retirement for one more post. This time I’ve actually been asked to bring back my JABBA awards by Martin Hunt (@martibartfast)…and with good reason!

There is a new preprint in bioRxiv…

I’m almost lost for words about this one. You know that it is a tenuous attempt at an acronym or initialism when you don’t use any letters from the 2nd, 3rd, 4th, or 5th words of the full software name!

The approach here is very close to just choosing a random five-letter word. The authors could also have had:

CLAMP: hierarChical taxonomic cLassification for virAl Metagenomic data via deeP learning

HOTEL: hierarcHical taxOnomic classificaTion for viral mEtagenomic data via deep Learning

RAVEN: hieraRchical tAxonomic classification for Viral metagenomic data via dEep learNing

ALIEN: hierArchical taxonomic cLassification for vIral metagEnomic data via deep learniNg

LARVA: hierarchicaL taxonomic classificAtion for viRal metagenomic data Via deep leArning

Okay, as this might be my only blog post of 2020, I’ll say CHEERio!

DOGMA: a new tool for assessing the quality of proteomes and transcriptomes

A new tool, recently published in Nucleic Acids Research, caught my eye this week:

The tool, by a team from the University of Münster, uses protein domains and domain arrangements in order to assess 'completeness' of a proteome or transcriptome. From the abstract…

Even in the era of next generation sequencing, in which bioinformatics tools abound, annotating transcriptomes and proteomes remains a challenge. This can have major implications for the reliability of studies based on these datasets. Therefore, quality assessment represents a crucial step prior to downstream analyses on novel transcriptomes and proteomes. DOGMA allows such a quality assessment to be carried out. The data of interest are evaluated based on a comparison with a core set of conserved protein domains and domain arrangements. Depending on the studied species, DOGMA offers precomputed core sets for different phylogenetic clades

Unlike CEGMA and BUSCO, which run against unannotated assemblies, DOGMA first requires a set of gene annotations. The paper focuses on the web server version of DOGMA but you can also access the source code online.

It's good to see that other groups are continuing to look at new ways of asssessing the quality of large genome/transcriptome/proteome datasets.

What's in a name?

Initially, I thought the name was just a word that both echoed 'CEGMA' and reinforced the central dogma of molecular biology. Hooray I thought, a bioinformatics tool that just has a regular word as a name without relying on contrived acronyms.

Then I saw the website…

  • DOGMA: DOmain-based General Measure for transcriptome and proteome quality Assessment

This is even more tenuous than the older, unrelated, version of DOGMA:

  • DOGMA: Dual Organellar GenoMe Annotator

Beyond Generations: My Vocabulary for Sequencing Tech →

Many writers have attempted to divide Next Generation Sequencing into Second Generation Sequencing and Third Generation Sequencing. Personally, I think it isn't helpful and just confuses matters. I'm not the biggest fan of Next Generation Sequencing (NGS) to start with, as like "post-modern architecture" (or heck, "modern architecture") it isn't future-proofed.

Keith Robison gives an interesting deep dive on how sequencing technologies have been named and potentially could be named.

This post reminded me of my previous takes on the confusing, and inconsistent labelling of these technologies: