Academic link rot seems to be getting faster: should a published URL last more than 100 days?

Consider this paper that was recently published in the journal Bioinformatics, and which showed up today in my RSS feed:

Presumably it is a typo when the journal says that it was received on November 14th 2014:

I'll assume that this is meant to be 2013! The paper first appeared online on June 13th 2014, just 103 days ago. The text of this paper links to some software that should be available at http://ww2.cs.mu.oz.au/∼gwong/LICRE. Except that this URL doesn't work. Neither does http://ww2.cs.mu.oz.au/∼gwong/. Only when I visit http://ww2.cs.mu.oz.au/ do I discover the following:

The new website for the merged departments says that the merger happened in 2012, and this is confirmed by the redirection page which has a date of 18th January 2012. It is also confirmed by looking at the Internet Archive's Wayback Machine which shows that the redirection page has been in place since at least February 2012. 

All of which suggests that the software link in the paper may have not even worked properly at the time they submitted the manuscript. I'm sure there are other similar examples of speedy link rot, but this seems particularly striking. Especially since a search for 'LICRE' on the new website doesn't return any hits (nor can I find any mention of it on Google or various search engine caches).

I will contact the lead author to let him know about the disappearance of the software. In the meantime, I'll remind people of this previous post of mine:

Update 2014-09-24 19.52:  I heard back from the author, the LICRE code is now at https://sites.google.com/site/licrerepository/

Another CEGMA post: KOGs vs CEGs and 458 vs 248

I posted another answer about CEGMA on seqanswers.com last week. I thought I'd cover this in a little more detail here (note, questions edited from how they originally appeared):

Question 1: CEGMA uses a 'kogs.fa' file — containing 2,748 proteins — to compare to a user's genome sequence. These KOGs define a set of 458 core eukaryotic genes (CEGs). Some CEGMA publications present the number of 458 CEGs that are present, others list results from the 248 most-highly-conserved CEGS. Does anyone know why kogs.fa is the default? Does it get 'curated' down to a smaller set during a CEGMA run?

The kogs.fa file represents a subset of the published set of 4,852 KOGs (euKaryotic Orthologous Groups). The KOGs database — which is still available online — describes protein groups that are present among seven different eukaryotes (not all groups are present in all species). We excluded data from the microsporidian Encephalitozoon cuniculi as it is a parasite and may have an atypical protein complement and focused on the 1,788 groups that were present in all of the remaining six species. We then applied various filtering criteria — see methods in original paper — to reduce this to the 458 KOGs (renaming this subset as CEGs in the process). We also chose just one protein to represent each species.

So that's why our kogs.fa file contains 2,748 proteins (458 x 6). CEGMA tries to determine which of these 458 CEGs are present in your input file. It's worth pointing out that the original purpose of CEGMA was to try to find a handful of genes in a genome which may lack gene annotations. Someone could then use this small gene set to train a gene finder, by which to annotate the entire genome.

After CEGMA has found which of the 458 CEGs are present, it then performs its secondary role of assessing the completeness of the gene space. To do this, it only wants to use the most conserved, and least paralogous of the 458 CEGs. Paralogy is a big issue here. The original KOGs database grouped together proteins when there were often many, many paralogs for each group. E.g. KOG0001 corresponds to the Ubiquitin gene family. Here are how many proteins occur in each of the seven species that represent this KOG:

  • Arabidopsis thaliana - 28
  • Caenorhabditis elegans - 12
  • Drosophila melanogaster - 3
  • Encephalitozoon cuniculi - 1
  • Homo sapiens - 17
  • Saccharomyces cerevisiae - 2
  • Schizosaccharomyces pombe - 1

The high degree of paralogy from A. thaliana is one reason why this KOG is not included in our subset of 248 CEGs. In contrast, KOG0018  — Structural maintenance of chromosome protein 1 (sister chromatid cohesion complex Cohesin, subunit SMC1) — is included in the 248 CEGs:

  • Arabidopsis thaliana - 1
  • Caenorhabditis elegans - 4
  • Drosophila melanogaster - 1
  • Encephalitozoon cuniculi - 1
  • Homo sapiens - 3
  • Saccharomyces cerevisiae - 1
  • Schizosaccharomyces pombe - 1

This secondary role of CEGMA uses information in the completeness_cutoff.tbl file (inside the CEGMA data directory) to narrow the 458 CEGs results down to a subset of 248 CEGs. Because different filtering criteria are used, a CEG may be classed as present in the 458 CEG set, but not in the 248 CEG set, even if it was on the list of 248 candidate CEGs.

Question 2: CEGMA output includes many KOG IDs but no descripition of what protein name/function each KOG ID represents. This makes it not so useful for annotating new genomes. Is there a lookup table somewhere?

One of the reason why we maintained KOG identifiers in the CEGMA output was so that people could, if so inclined, look up more information in the KOGs database. If you download the 'kog' file from the KOGs database, you will see each KOG has a one line description. E.g.

[O] KOG0019 Molecular chaperone (HSP90 family)
[KC] KOG0025 Zn2+-binding dehydrogenase (nuclear receptor binding factor-1)
[ZD] KOG0028 Ca2+-binding protein (centrin/caltractin), EF-Hand superfamily protein
[C] KOG0042 Glycerol-3-phosphate dehydrogenase
[T] KOG0044 Ca2+ sensor (EF-Hand superfamily)
[K] KOG0048 Transcription factor, Myb superfamily

The letters inside square brackets, represent various functional categories annotated by the KOGs database. These are as follows:

INFORMATION STORAGE AND PROCESSING
 [J] Translation, ribosomal structure and biogenesis
 [A] RNA processing and modification
 [K] Transcription
 [L] Replication, recombination and repair
 [B] Chromatin structure and dynamics

CELLULAR PROCESSES AND SIGNALING
 [D] Cell cycle control, cell division, chromosome partitioning
 [Y] Nuclear structure
 [V] Defense mechanisms
 [T] Signal transduction mechanisms
 [M] Cell wall/membrane/envelope biogenesis
 [N] Cell motility
 [Z] Cytoskeleton
 [W] Extracellular structures
 [U] Intracellular trafficking, secretion, and vesicular transport
 [O] Posttranslational modification, protein turnover, chaperones

METABOLISM
 [C] Energy production and conversion
 [G] Carbohydrate transport and metabolism
 [E] Amino acid transport and metabolism
 [F] Nucleotide transport and metabolism
 [H] Coenzyme transport and metabolism
 [I] Lipid transport and metabolism
 [P] Inorganic ion transport and metabolism
 [Q] Secondary metabolites biosynthesis, transport and catabolism

POORLY CHARACTERIZED
 [R] General function prediction only
 [S] Function unknown

Maybe this is useful to someone. However, I would remind people that KOGs was published over a decade ago (and presumably the work to generate the KOGs database begun in 2002 if not earlier). There were probably several gene annotations that were missing in the source genomes at that time, and many annotations have presumably since been updated (I bet many genes have had minor alterations to their structure).

 

Updated version of my 'Genome assembly: then and now' talk is now available

This is a presentation that I have probably given five times now. Originally, the main focus of the talk was purely about the Assemblathon 2 paper, with some thoughts about how the field of genome assembly has changed since the days of Sanger-only sequencing.

Over time, I've increasingly downplayed the Assemblathon 2 content of the talk, and made way for updates relating to the latest developments in genome sequencing and assembly. To that end, I've decided to start adding version numbers to this talk to help make it easier to distinguish between different versions.

So here is version 1.2 of my talk, presented below with and without notes (my talks are very visual, so I have embedded notes to try to capture what I talk about for each slide). Don't be put off by the high slide count (many of these just reflect animated steps).

Without notes…

With notes (probably need to go full-screen to be able to clearly read these)…

Too many genome assemblers to keep track of? Nucleotid.es to the rescue!

Yesterday, I presented an updated version of my 'Genome Assembly: Then and Now' talk. I'll try to post the full set of slides (with notes) later today on Slideshare. But I thought I'd share just one of the new slides from the talk; here are six papers that describe new genome assembly tools…

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