Linking NCBI taxonomy to GBIF

@rdmpage, given an NCBI taxon ID, how to get GBIF occurrence records via web service?

— Rutger Vos (@rvosa) May 30, 2012

In response to Rutger Vos's question I've started to add GBIF taxon ids to the iPhylo Linkout website. If you've not come across iPhylo Linkout, it's a Semantic Mediawiki-based site were I maintain links between the NCBI taxonomy and other resources, such as Wikipedia and the BBC Nature Wildlife finder. For more background see

Page, R. D. M. (2011). Linking NCBI to Wikipedia: a wiki-based approach. PLoS Currents, 3, RRN1228. doi:10.1371/currents.RRN1228

I'm now starting to add GBIF ids to this site. This is potentially fraught with difficulties. There's no guarantee that the GBIF taxonomy ids are stable, unlike NCBI tax_ids which are fairly persistent (NCBI publish deletion/merge lists when they make changes). Then there are the obvious problems with the GBIF taxonomy itself. But, if you want a way to generate a distribution map for a taxon in the NCBI taxonomy, the quickest way is going to be via GBIF.

The mapping is being made automatically, with some crude checks to try and avoid too many erroneous links (e.g., due to homonyms). It will probably take a few days to complete (the mapping is quick, uploading to the wiki is a bit slower). Using a wiki to manage the mapping makes it easy to correct any spurious matches.

As an example, the page http://iphylo.org/linkout/Ncbi:109175 is for the frog Hyla japonica (NCBI tax_id 109175) and shows links to Wikipedia (http://en.wikipedia.org/wiki/Japanese_Tree_Frog, and to GBIF (http://data.gbif.org/species/2427601/). There's even a link to TreeBASE. I display a GBIF map so you can see what data GBIF currently has for that taxon.



So, we have a wiki page, how do we answer Rutger's original question: how to get GBIF occurrence records via web service?

To do this we can use the RDF output by the Semantic Mediawiki software that underpins the Wiki. You can gte this by clicking on the RDF icon near the bottom of the page, or go to http://iphylo.org/linkout/Special:ExportRDF/Ncbi:109175. The RDF this produces is really, really ugly (and people wonder why the Semantic Web has been slow to take off...). In this RDF you will see the statement:

<rdfs:seeAlso rdf:resource="http://data.gbif.org/species/2427601/"/>

So, arm yourself with XPath, a regular expression, or if you are a serious RDF geek break out the SPARQL, and you can extract the GBIF taxon id for a NCBI taxon. Given that id you can query the GBIF web services. One service that I like is the occurrence density service, which you can use to recreate the 1°×1° density maps shown by GBIF. For example, http://data.gbif.org/ws/rest/density/list?taxonconceptkey=2427601 will get you the squares shown in the screen shot above.

Of course, I have glossed over several issues, such as the errors and redundancy in the GBIF classification, the mismatch between NCBI and GBIF classifications (NCBI has many more ranks than GBIF), and whether the taxon concepts used by the two databases are equivalent (this is likely to be more of an issue for higher taxa). But it's a start.

Dark taxa even darker: NCBI pulls (some) DNA barcodes from GenBank (updated)

Dark taxa have become even darker. NCBI has pulled the plug on large numbers of DNA barcode sequences that lack scientific names. For example, taxon Cyclopoida sp. BOLD:AAG9771 (tax_id 818059) now has a sparse page that has no associated sequences. From an earlier download of EMBL I know that this taxon is associated with at least 5 sequences, such as GU679674. But if you go to that sequence you get this:



So the the sequence is hidden. You can retrieve it by clicking on the obsolete version link, but by default it is hidden.

It's an extraordinary state of affairs that a huge slice of fundamental biodiversity data has been effectively "pulled" from view.

UpdateSujeevan Ratnasingham from iBOL has pointed out that the sequence I'd used above (GU679674) was not one of the ones hidden by NCBI, rather it was suppressed at the request of the investigator (which I'd have realised if I'd paid more attention to the screenshot). HQ918317 is an example of a BOLD record that was suppressed:

Linking GBIF and Genbank

As part of my mantra that it's not about the data, it's all about the links between the data, I've started exploring matching GenBank sequences to GBIF occurrences using the specimen_voucher codes recorded in GenBank sequences. It's quickly becoming apparent that this is not going to be easy. Specimen codes are not unique, are written in all sorts of ways, there are multiple codes for the same specimen (GenBank sequences may be associated with museum catalogue entries, or which field or collector numbers).

So why undertake what is fast looking like a hopeless task? There are several reasons:

1. GBIF occurrences have a unique URL which we could potentially use as a unique, resolvable identifier for the corresponding specimen.
2. Linking GenBank to GBIF would make it possible for GBIF to list sequences associated with a specimen, as well as the associated publication, which means we could demonstrate the "impact" of a specimen. In the simplest terms this could be the number of sequences and publications that use data from the specimen, more sophisticated approaches could use PageRank-like measures, see hdl:10101/npre.2008.1760.1.
3. Having a unique identifier that is shared across different databases makes it easier to combine data from different sources. For example, if a sequence in GenBank lacks geographic coordinates but the voucher specimen in GBIF is georeferenced, we can use that information to locate the sequence in geographic space (and hence build geophylogenies or add spatial indexes to databases such as TreeBASE). Conversely, if the GenBank sequence is georeferenced but the GBIF record isn't we can update the GBIF record and possibly expand the range of the corresponding taxon (this was part of the motivation behind hdl:10101/npre.2009.3173.1.

As an example, below is the GBIF 1° density map for the frog Pristimantis ridens from GBIF, with the phylogeny from Wang et al.Phylogeography of the Pygmy Rain Frog (Pristimantis ridens) across the lowland wet forests of isthmian Central Americahttp://dx.doi.org/10.1016/j.ympev.2008.02.021 layered over it. I created the KML tree from the corresponding tree in TreeBASE using the tool I described earlier. You can grab the KML for the tree here.



As we'd expect, there is a lot of overlap in the two sources of data. If we investigate further, there are records that are in fact based on the same specimen. For example, if we download the GBIF KML file with individual placemarks we see that in the northern part of the range their are 15 GBIF occurrences that map onto the same point as one of the terminal taxa in the tree.



One of these 15 GBIF records (http://data.gbif.org/occurrences/244335848) is for specimen USNM 514547, which is the voucher specimen for EU443175. This gives us a link between the record in GBIF and the record in GenBank. It also gives us a URI we can use for the specimen http://data.gbif.org/occurrences/244335848 instead of the unresolvable and potentially ambiguous USNM 514547.

If we view the geophylogeny from a different vantage point we see numerous localities that don't have occurrences in GBIF.



Close inspection reveals that some of the specimens listed in the Wang et al. paper are actually in GBIF, but lack geographic coordinates. For example the OTU "Pristimantis ridens Nusagandi AJC 0211" has the voucher specimen FMNH 257697. This specimen is in GBIF as http://data.gbif.org/occurrences/57919777/, but without coordinates, so it doesn't appear on the GBIF map. However, both the Wang et al. paper and the GenBank record for the sequence from this specimen EU443164 give the latitude and longitude. In this example, GBIF gives us a unique identifier for the specimen, and GenBank provides data on location that GBIF lacks.

Part of GBIFs success is due to the relative ease of integrating data by taxonomic names (despite the problems caused by synonyms, homonyms, misspellings, etc.) or using spatial coordinates (which immediately enables integration with environmental data. But if we want to integrate at deeper levels then specimen records are the glue that connects GBIF (and its contributing data sources) to sequence databases, phylogenies, and the taxonomic literature (via lists of material exampled). This will not be easy, certainly for legacy data that cites ambiguous specimen codes, but I would argue that the potential rewards are great.

TDWG Challenge - what is RDF good for?

Last month, feeling particularly grumpy, I fired off an email to the TDWG-TAG mailing list with the subject Lobbing grenades: a challenge. Here's the email:

It's morning and the coffee hasn't quite kicked in yet, but reading through recent TDWG TAG posts, and mindful of the upcoming meeting in New Orleans (which sadly I won't be attending) I'm seeing a mismatch between the amount of effort being expended on discussions of vocabularies, ontologies, etc. and the concrete results we can point to.

Hence, a challenge:

"What new things have we learnt about biodiversity by converting biodiversity data into RDF?"

I'm not saying we can't learn new things, I'm simply asking what have we learnt so far?

Since around 2006 we have had literally millions of triples in the wild (uBio, ION, Index Fungorum, IPNI, Catalogue of Life, more recently Biodiversity Collections Index, Atlas of Living Australia, World Register of Marine Species, etc.), most of these using the same vocabulary. What new inferences have we made?

Let's make the challenge more concrete. Load all these data sources into a triple store (subchallenge - is this actually possible?). Perhaps add other RDF sources (DBpedia, Bio2RDF, CrossRef). What novel inferences can we make?

I may, of course, simply be in "grumpy old arse" mode, but we have millions of triples in the wild and nothing to show for it. I hope I'm not alone in wondering why...

In the context of the TDWG meeting (happening as we speak and which I'm following via Twitter, hashtag #tdwg) Joel Sachs asked me whether I had any specific data in mind that could form the basis of a discussion. So, here goes. I've assembled some small RDF data sets that it might be fun to play with. Each data set is for frogs, and I've divided them into two sets.

Primary data
These data sets are essentially unmodified RDF fetched from data providers:

• uniprot.rdf Uniprot RDF for frogs in GenBank
• ion.rdf Index of Organism Names (ION) RDF for taxonomic names for frogs (filtered to just those names that are also in GenBank, the RDF comes from ION LSIDs)
• crossref.rdf CrossRef RDF for DOIs for publications that published new frog names (obtaining using CrossRef's support for Linked Data for DOIs)
• dbpedia.rdf Dbpedia RDF for frogs in GenBank (Update 2011-10-20: the dbpedia.rdf file is a bit big, so here is subset.rdf which has just the conservation status and thumbnail image)

These sources give us information on genomics (at least, they tell us which taxa have been sequenced), where and when the original taxonomic description was published, and by whom, as well as some information on conservation status and what the frog looks like (via Dbpedia). Ideally we just load these files into a triple store and then ask a bunch of questions, such as what is the conservation status of frogs sequenced in Genbank?, is there correlation between the conservation status of a frog and the date it was discovered?, who has described the most frog species?, etc.

My contention is that actually we can't do any of this because the data is siloed due to the lack of shared identifiers and vocabularies (I suspect that there is not a single identifier any of these files share). The only way we can currently link these data sets together is by shared string literals (e.g., taxonomic names), in which case why bother with RDF? So my first challenge is to see whether any of the questions I've just listed can actually be tackled using this data.

Glue
In a slightly more constructive mode, to see if we can make progress I'm providing some additional RDF files, based on projects I'm working on to link data together. These files may help provide some of the missing "glue" to connect these data sets.

• linkout.rdf The list of links between NCBI and Dbpedia (based on mapping in iPhylo LinkOut)
• ion_doi.rdf A subset of publications listed in ION have DOIs, this file links the corresponding ION LSIDs to those DOIs (this file is from an ongoing project mapping names to primary literature)

The first file links the ION and CrossRef RDF, so we could start to ask questions about dates of discovery, who described what species, etc.. The second file links NCBI taxon ids (in this case in the form of UniProt URIs) to Wikipedia (in the form of Dbpedia URIs). Dbpedia has information on conservation status, and some frogs will also have pictures, so we can start to join genomics to conservation, as well as make some visualisations.

Update
I've now added another RDF file for 1000 georeferenced GenBank sequences for frogs. The file is genbank.rdf. This file is generated from a local, processed version of EMBL, and uses a mixture of Dublin Core and TDWG vocabularies. Here's an example of a single record:

<?xml version="1.0"?>
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" 
xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" 
xmlns:owl="http://www.w3.org/2002/07/owl#" 
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" 
xmlns:tcommon="http://rs.tdwg.org/ontology/voc/Common#" 
xmlns:toccurrence="http://rs.tdwg.org/ontology/voc/TaxonOccurrence#" 
xmlns:uniprot="http://purl.uniprot.org/core/">
  <uniprot:Molecule rdf:about="http://bio2rdf.org/genbank:EU566842">
    <dcterms:created>2008-07-06</dcterms:created>
    <dcterms:modified>2010-12-23</dcterms:modified>
    <dcterms:title>EU566842</dcterms:title>
    <dcterms:description>Xenopus borealis voucher MHNG:Herp:2644.64 
cytochrome oxidase subunit I (COI) gene, partial cds; mitochondrial.</dcterms:description>
    <dcterms:subject rdf:resource="http://purl.uniprot.org/taxonomy/8354"/>
    <dcterms:relation rdf:parseType="Resource">
      <rdf:type rdf:resource="http://rs.tdwg.org/ontology/voc/TaxonOccurrence#TaxonOccurrence"/>
      <toccurrence:identifiedToString>Xenopus borealis</toccurrence:identifiedToString>
      <toccurrence:decimalLatitude>0.66</toccurrence:decimalLatitude>
      <geo:lat>0.66</geo:lat>
      <toccurrence:decimalLongitude>37.5</toccurrence:decimalLongitude>
      <geo:long>37.5</geo:long>
      <toccurrence:verbatimCoordinates>0.66 N 37.5 E</toccurrence:verbatimCoordinates>
      <toccurrence:country>Kenya</toccurrence:country>
      <dcterms:identifier>MHNG:Herp:2644.64</dcterms:identifier>
    </dcterms:relation>
  </uniprot:Molecule>
</rdf:RDF>

I've added this simply so one could do some geographical queries.

Missing links
There are still lots of missing links here (for example, there's no explicit link between NCBI and ION, so we'd need to create this using taxonomic names), and we could add further links to the literature via sequences for taxa. Then there's the lack of geographic data. We could get some of this via georeferenced sequences in GenBank, but there's no RDF for this (Bio2RDF does have RDF for sequences but it ignores the bulk of the organismal metadata such as voucher specimens and latitude and longitude).

In many ways it's this lack of links that was point of my original email. The reality is that "linked data" isn't linked to anything like the extent that makes it useful. Simply pumping out RDF won't get us very far until we tackle this problem (see also my earlier post Linked data that isn't: the failings of RDF).

So, if you think RDF is the way to go, please tell me what you can learn from these data files.

Tree of Life 0.1 - annotating the NCBI taxonomy

Last week I was at the NSF "Assembling, Visualising and Analysing the Tree of Life" Ideas Lab, run by KnowInnovation.com/. It was an interesting experience, essentially a structured week of brainstorming ideas.

One thing I came away with is the feeling that our notions of the "tree of life" are fuzzy, contradictory, and often probably unobtainable. It's tempting to imagine all sorts of wonderful visualisations, and loose sight of building something that is useful. Perhaps it's time instead to think of "Tree of Life version 0.1".

Imagine taking the NCBI taxonomy as a starting point. Yes it's incomplete, and has almost no fossils, but it's freely available and linked to a lot of data. Let's use a Google Maps-like viewer along the lines I explored earlier this year.

Then add annotation "tracks" to the tips. As a first pass these could be taken from the NCBI LinkOut service, such as the NCBI-Wikipedia mapping http://iphylo.org/linkout.



The NCBI tree is a classification rather than a phylogeny, so we could add greater phylogenetic content by linking to phylogenetic databases, such as TreeBASE and PhyLoTA. Imagine clicking on a node in the NCBI taxonomy and seeing a display of all the phylogenies centred on that node:



Now we have a way to navigate a large tree, view annotations, and display phylogenetic trees. All of this could be done fairly easily. The key is to have services keyed by the NCBI tax_id used to identify nodes on the tree.

Among the next steps would be to add additional "tracks", perhaps based on curated links analogous to the wiki-based NCBI-Wikipedia mapping. For example, very basic habitat data (marine or terrestrial) could be added, or geography, or host relationships (could be based in part on the data already in GenBank).

Given that the NCBI tree continues to grow, subsequent versions could be released as the tree changes. Or we could "fork" the NCBI tree and start to refine it based on phylogenetic information, and add taxa that aren't in the genome databases (these taxa will need consistent identifiers so we can map annotations on to them as well). Perhaps we could use something like Git to manage this tree, and to handle the necessary merging of updated versions of the NCBI tree. People could edit the tree, or indeed fork it and come up with their own.

There are lots of ways to visualise trees (see TreeVis.net for some great examples), but what I'm after is a tool that is useful, that gives us a sense of what we know and what we don't. I suspect that one of the reasons we've struggled with visualising the tree of life is that there are lots of different notions about what it's for. In this case, I want a tool to navigate data about organisms, one that we can easily add annotations too.

Dark taxa: GenBank in a post-taxonomic world

In an earlier post (Are names really the key to the big new biology?, I questioned Patterson et al.'s assertion in a recent TREE article (doi:10.1016/j.tree.2010.09.004) that names are key to the new biology.

In this post I'm going to revisit this idea by doing a quick analysis of how many species in GenBank have "proper" scientific names, and whether the number of named species has changed over time. My definition of "proper" name is a little loose: anything that had two words, second one starting with a lower case letter, was treated as a proper name. hence, a name like Eptesicus sp. A JLE-2010" is not a proper name, but Eptesicus andersoni is.

Mammals

Since GenBank started, every year has seen some 100-200 mammal species added to the database.


Until around 2003 almost all of these species had proper binomial names, but since then an increasing percentage of species-level taxa haven't been identified to species. In 2010 three-quarters of new tax_ids for mammals weren't identified.

Invertebrates

For "invertebrates" 2010 saw an explosive growth in the number of new taxa sequenced, with nearly 71,000 new taxa added to GenBank.



This coincides with a spectacular drop in the number of properly-named taxa, but even before 2010 the proportion of named invertebrate species in GenBank was in decline: in 2009 just over a half of the species added had binomials.

Bacteria

To put this in perspective, here are the equivalent graphs for bacteria.
Although at the outset most of the bacteria in GenBank had binomial names, pretty quickly the bulk of sequenced bacteria had informal names. In 2010 less than 1% of newly sequenced bacteria had been formerly described.

Dark taxa

For bacteria the graphs are hardly surprising. To get a proper name a bacterium must be cultured, and the vast majority of bacteria haven't been (or can't be) cultured. Hence, microbiologists can gloat at the nomenclatural mess plant and animal taxonomists have to deal with only because microbiologists have a tiny number of names to deal with.

For mammals and invertebrates there's clear a decline in the use of proper names.It would be tempting to suggest that this reflects a decline in the number of taxonomists - there might simply not be enough of them in enough groups to be able to identify and/or describe the taxa being sequenced.

However, if we look at the recent peaks of unnamed animal species, we discover that many have names like Lepidoptera sp. BOLD:AAD7075, indicating that they are DNA Barcodes from the Barcode of Life Data Systems. Of the 62,365 unnamed invertebrates added last year, 54,546 are BOLD sequences that haven't been assigned to a known species. Of the 277 unnamed mammals, 218 are BOLD taxa. Hence, DNA bnacording is flooding Genbank with taxa that lack proper names (and typically are represented by a single DNA bnacode sequence).

There are various ways to interpret these graphs, but for me the message is clear. The bulk of newly added taxa in GenBank are what we might term "dark taxa", that is, taxa that aren't identified to a known species. This doesn't necessarily mean that they are species new to science, we may already have encountered these species before, they may be sitting in museum collections, and have descriptions already published. We simply don't know. As the output from DNA barcoding grows, the number of dark taxa will only increase, and macroscopic biology starts to look a lot like microbiology.


A post-taxonomic world
If we look at the graphs for bacteria, we see that taxonomic names are virtually irrelevant, and yet microbiology seems to be doing fine as a discipline. So, perhaps it's time to think about a post-taxonomic world where taxonomic names, contra Patterson et al., are not that important. We can discover a good deal about organismal biology from GenBank alone (see my post Visualising the symbiome: hosts, parasites, and the Tree of Life for some examples, as well as Rougerie et al. 2010 doi:10.1111/j.1365-294X.2010.04918.x).

This leaves us with two questions:

1. How much biology can we do without taxonomic names?
2. If the lack of taxonomic names limits what we can do (and, playing devil's advocate, this is an open question) how can we speed up linking GenBank sequences to names?

I suspect that the answer to (1) is "quite a lot" (especially if we think like microbiologists). Question (2) is ultimately a question about how fast we can link literature, museum collections, sequences, and phylogenies. If progress to date is any indication, we need to rethink how we do this, and in a hurry, because dark taxa are accumulating at an accelerating rate.

How the analyses were done

Although the NCBI makes a dump of its taxonomic database available via FTP (at ftp://ftp.ncbi.nih.gov/pub/taxonomy/), this dump doesn't have dates for when the taxa were added to the database. However, using the Entrez EUtilities we can get the tax_ids that were published within a given date range. For example, to retrieve all the tax_ids added to the database in December 2010, we set the URL parameters &mindate=2010/12/01 and &maxdate=2010-12-31 to form this URL:

http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=taxonomy&mindate=2010/12/01&maxdate=2010/12/31&retmax=1000000.

I've set &retmax to a big number to ensure I get all the tax_ids for that month (in this case 23511). I then made a local copy of the NCBI database in MySQL ( instructions here) and queried for all species-level taxa in GenBank. I used a rather crude regular expression REGEXP '^[A-Z][a-z]+ [a-z][a-z]+$' to find just those species names that were likely to be proper scientific names (i.e., no "sp.", "aff.", museum or voucher codes, etc.). To group the species into major taxonomic groups I used the division_id.

Results are available in a Google Spreadsheet.

Paper on NCBI and Wikipedia published in PLoS Currents: Tree of Life

My paper describing the mapping between NCBI and Wikipedia has been published in PLoS Currents: Tree of Life. You can see the paper here. It's only just gone live, so it's yet to get a PubMed Central number (one of the nice features of PLoS Currents is that the articles get archived in PMC).

Publishing in PLoS Currents: Tree of Life was a pleasant experience. The Google Knol editing environment was easy to use, and the reviewing process quick. It's obviously a new and rather experimental journal, and there are a few things that could be improved. Automatically looking up articles by PubMed identifier is nice, but it would also be great to do this for DOIs as well. Furthermore, the PubMed identifiers aren't displayed as clickable links, which rather defeats the point of having references on the web (I've added DOI links to the articles wherever possible). But, minor grumbles aside, as a way to get an Open Access article published for free, and have it archived in PubMed Central, PLoS Currents is hard to beat. What will be interesting is whether the article receives any comments. This seems to be one area online journals haven't really cracked — providing an environment where people want to engage in discussion.

TreeBASE meets NCBI, again

Déjà vu is a scary thing. Four years ago I released a mapping between names in TreeBASE and other databases called TBMap (described here: doi:10.1186/1471-2105-8-158). Today I find myself releasing yet another mapping, as part of my NCBI to Wikipedia project. By embedding the mapping in a wiki, it can be edited, so the kinds of problems I encountered with TbMap, recounted here, here, and here. The mapping in and of itself isn't terribly exciting, but it's the starting point for some things I want to do regarding how to visualise the data in TreeBASE.

Because TreeBASE 2 has issued new identifiers for its taxa (see TreeBASE II makes me pull my hair out), and now contains its own mapping to the NCBI taxonomy, as a first pass I've taken their mapping and added it to http://iphylo.org/linkout. I've also added some obvious mappings that TreeBASE has missed. There are a lot more taxa which could be added, but this is a start.

The TreeBASE taxa that have a mapping each get their own page with a URL of the form http://iphylo.org/linkout/<TreeBase taxon identifier>, e.g. http://iphylo.org/linkout/TB2:Tl257333. This page simply gives the name of the taxon in TreeBASE and the corresponding NCBI taxon id. It uses a Semantic Mediawiki template to generate a statement that the TreeBASE and and NCBI taxa are a "close match". If you go to the corresponding page in the wiki for the NCBI taxon (e.g., http://iphylo.org/linkout/Ncbi:448631) you will see any corresponding TreeBASE taxa listed there. If a mapping is erroneous, we simply need to edit the TreeBASE taxon page in the wiki to fix it. Nice and simple.

At the time of writing the initial mapping is still being loaded (this can take a while). I'll update this post when the uploading has finished.

TreeBASE, again

My views on TreeBASE are pretty well known. Lately I've been thinking a lot about how to "fix" TreeBASE, or indeed, move beyond it. I've made a couple of baby steps in this direction.

The first step is that I've created a group for TreeBASE papers on Mendeley. I've uploaded all the studies in TreeBASE as of December 13 (2010). Having these in Mendeley makes it easier to tidy up the bibliographic metadata, add missing identifiers (such as DOIs and PubMed ids), and correct citations to non-existent papers (which can occur if at the time the authors uploaded their data the planned to submit their paper to one journal, but it ending up being accepted in another). If you've a Mendeley account, feel free to join the group. If you've contributed to TreeBASE, you should find your papers already there.

The second step is playing with CouchDB (this years new hotness), exploring ways to build a database of phylogenies that has nothing much to do with either a relational database or a triple store. CouchDB is a document store, and I'm playing with taking NeXML files from TreeBASE, converting them to something vaguely usable (i.e., JSON), and adding them to CouchDB. For fun, I'm using my NCBI to Wikipedia mapping to get images for taxa, so if TreeBASE has mapped a taxon to the NCBI taxonomy, and that taxon has a page in Wikipedia with an image, we get an image for that taxon. The reason for this is I'd really like a phylogeny database that was visually interesting. To give you some examples, here are trees from TreeBASE (displayed using SVG), together with thumbnails of images from Wikipedia:

Everything (tree and images) is stored within a single document in CouchDB, making the display pretty trivial to construct. Obviously this isn't a proper interface, and there's things I'd need to do, such as order the images in such a way that they matched the placement of the taxa on the tree, but at a glance you can see what the tree is about. We could then envisage making the images clickable so you could find out more about that taxon (e.g., text from Wikipedia, lists of other trees in the database, etc.).

We could expand this further by extracting geographical information (say, from the sequences included in the study) and make a map, or eventually a phylogeny on Google Earth) (see David Kidd's recent "Geophylogenies and the Map of Life" for a manifesto doi:10.1093/sysbio/syq043).

One of the big things missing from databases like TreeBASE is a sense of "fun", or serendipity. It's hard to find stuff, hard to discover new things, make new connections, or put things in context. And that's tragic. Try a Google image search for treebase+phylogeny:

Call me crazy, but I looked at that and thought "Wow! This phylogeny stuff is cool!" Wouldn't it be great if that's the reaction people had when they looked at a database of evolutionary trees?

Mashing up NCBI and Wikipedia using treemaps

Having made a first stab at mapping NCBI taxa to Wikipedia, I thought it might be fun to see what could be done with it. I've always wanted to get quantum treemaps working (quantum treemaps ensure that the cells in the treemap are all the same size, see my 2006[!] blog post for further description and links). After some fussing I have some code that seems to do the trick. As an example, here is a quantum treemap for Laurasiatheria.


The diagram shows the NCBI taxonomy subtree rooted on Laurasiatheria, with images (where available) from Wikipedia for the children of the the children of that node. In other words, the images correspond to the tips of the tree below:



There's a lot to be done to tidy this up, but there is potential to create a nice, visual way to navigate through the NCBI taxonomy (it might work well on the iPhone or iPad, for example).

NCBI to Wikipedia links are now live...

The 52,956 links from NCBI to Wikipedia that I've been busy creating are now "live." If you go to a NCBI taxon such as Sphaerius you'll see something like this:

Clicking the "Wikipedia" link takes you to the Wikipedia page for this taxon. You can see all the links to Wikipedia using the query loproviphylo[filter]. Here are some additional links to try:

NCBI	Wikipedia
8353	Xenopus
83698	Banksia
9766	Balaenoptera

Thanks to Scott Federhen and Kathy Kwan at NCBI for all their assistance in getting this into NCBI Linkout.

Fixing errors
There will be errors and omissions. The best way to fix these is by using the iPhylo Linkout wiki. The page for a NCBI taxon is always http://iphylo.org/linkout/Ncbi:xxxx where xxxx is the NCBI taxonomy id. You can edit/annotate the link there (click on the "edit with form" for a simple web form). I plan to regularly update the links based on this the wiki.

Future
NCBI Linkout provide access statistics so it will be interesting to see how much traffic goes from NCBI to Wikipedia. It will also be interesting to see if this is correlated with increased editing of those Wikipedia pages.

Linking NCBI to Wikipedia

In an earlier post I discussed linking NCBI taxonomy to Wikipedia. One way to tackle this is to add NCBI Taxonomy ID to Wikipedia pages. I reopened the case for adding the Taxonomy IDs to the Taxobox on each taxon page, but this met with substantial resistance. A modified proposal to add them elsewhere to the Wikipedia page seems to be gaining more support (or, at least, less vigorous resistance).

Meanwhile, there are other things that need to be done to linking NCBI and Wikipedia. One is to add Wikipedia page names to NCBI Linkout so that when viewing a NCBI taxon page you will see a link to Wikipedia if a page for the corresponding taxon exists. To create this linkout we need a mapping from NCBI to Wikipedia, and that's what I've been working on for the last few days.

The mapping is still in progress, but essentially I've taken a dump of the NCBI taxonomy for June 3, 2010, and matched the names with those in a the June 18, 2009 dump of Wikipedia that I've analysed elsewhere on this blog. I'll detail the various steps in the mapping elsewhere (there are issues such as synonyms, homonyms, Wikipedia redirects, etc.), but for now things seem to be working reasonably well.

The mapping is being created in a Semantic Mediawiki at http://iphylo.org/linkout/. When complete you will be able to up a NCBI taxon by either it's name (including synonyms and common names) or it's NCBI Taxonomy ID. Where possible I'm mapping the NCBI taxon to Wikipedia, and providing a snippet of text and an image.

I've also extracted bibliographic information from the citations.dmp file that comes with the NCBI dump. This contains the comments that you sometimes see on a taxon page. In a few cases I've added some information manually. For example, the beetle genus Sphaerius has a rather complicated nomenclatural history, which the NCBI page summarises as:

Due to a recent ruling (ICZN 2000), the family and generic names Sphaeriusidae Erichson, 1845, and Sphaerius Waltl, 1838, are both available names and have priority over Microsporidae Crotch, 1873 for the family name and Microsporus Kolenati, 1846 for the single included genus, respectively.

By looking through BioStor I've found some of the papers relating to this ICZN ruling, and added them to the wiki page http://iphylo.org/linkout/Ncbi:174920 (aficionados of zoological nomenclature may enjoy the complexity of the case, due to homonymy between the corresponding family name, Sphaeriidae, and a mollusc family of the same name).

Once thus mapping is complete, it will be time to think of how to get this into NCBI's Linkout, and also how to automatically update the mapping to reflect the growth of both the NCBI taxonomy and Wikipedia. If you visit http://iphylo.org/linkout/ please be aware that the mapping is still being written to the wiki (this is being done via API calls, and adding some 900,000 pages is going to take a while).

NCBI Taxonomy IDs and Wikipedia

I've written a note on the Wikipedia Taxobox page making the case for adding NCBI taxonomy IDs to the standard Taxobox used to summarise information about a taxon. Here is what I wrote:

Wikipedia's taxon pages have a huge web presence (see my blog post Google and Wikipedia revisited and Page, R. D. M. (2010). "Wikipedia as an encyclopaedia of life". Nature Precedings hdl:10101/npre.2010.4242.1). If a taxon is in Wikipedia it is almost always the first search result in Google. Researchers in other areas of biology are making use of a Wikipedia as a tool to annotate genes Gene Wiki and RNA families Wikipedia:WikiProject_RNA, respectively. Pages for genes, such as Cytochrome_b, have numerous external identifiers in their equivalent of the Taxobox (the Pfam_box). I think we are missing a huge opportunity by not including NCBI taxonomy ids. The advantages would be:

• It would provide a valuable service to Wikipedia readers by enabling them to go to NCBI to discover more about a taxon


• It would help Wikipedia contributors by providing a standardised way to refer to NCBI (and enable bots to add missing NCBI taxonomy ids). Putting them in an External links section makes it harder to be consistent (there are various ways to write a URL linking to the NCBI taxonomy)


• It would facilitate linking from NCBI to Wikipedia. A mapping of Wikipedia pages to NCBI taxonomy ids could be added to NCBI Linkout, generating more traffic to the Wikipedia pages


• Projects that are trying to integrate information from different sources would be able to combine information of genomics from NCBI with other information much more readily

Note that I am not arguing that Wikipedia should "follow" NCBI taxonomy, merely that where the potential to link exists, the links would create value, both within and outside the Wikipedia community.

Some discussion has ensued on the Taxobox page, all positive. I'm blogging this here to encourage anyone who as any more thoughts on the matter to contribute to the discussion.

NCBI RDF

Following on from the last post, I've now set up a trivial NCBI RDF service at bioguid.info/taxonomy/ (based on the ISSN resolver I released yesterday and announced on the Bibliographic Ontology Specification Group).

If you visit it in a web browser it's nothing special. However, if you choose to display XML you'll see some simple RDF. I've mapped some NCBI fields to corresponding terms in ttp://rs.tdwg.org/ontology/voc/TaxonConcept# (including the deprecated rankString term, which really shouldn't be deprecated, IMHO). I've also extracted what LSIDs I can from any linkouts. For example, a name that appears in Index Fungorum will have the corresponding LSID, likewise for IPNI. URLs are simply listed as rdfs:seeAlso.

Here's the RDF for NCBI taxon 101855 (you can grab this from http://bioguid.info/taxonomy/101855):

<?xml version="1.0" encoding="utf-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
 xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" 
 xmlns:dcterms="http://purl.org/dc/terms/"
 xmlns:tcommon="http://rs.tdwg.org/ontology/voc/Common#" 
 xmlns:tc="http://rs.tdwg.org/ontology/voc/TaxonConcept#">
<tc:TaxonConcept rdf:about="taxonomy:101855 ">
<dcterms:title>Lulworthia uniseptata</dcterms:title>
<dcterms:created>1999-08-16</dcterms:created>
<dcterms:modified>2005-01-19</dcterms:modified>
<dcterms:issued>1999-09-14</dcterms:issued>
<tc:nameString>Lulworthia uniseptata</tc:nameString><tc:rankString>species</tc:rankString>
<tcommon:taxonomicPlacementFormal>cellular organisms, Eukaryota, Fungi/Metazoa group, Fungi, Dikarya, Ascomycota, Pezizomycotina, Sordariomycetes, Sordariomycetes incertae sedis, Lulworthiales, Lulworthiaceae, Lulworthia</tcommon:taxonomicPlacementFormal>
<tc:hasName rdf:resource="urn:lsid:indexfungorum.org:names:105488"/>
<rdfs:seeAlso rdf:resource="http://www.marinespecies.org/aphia.php?p=taxdetails&id=100407"/>
<rdfs:seeAlso rdf:resource="http://www.mycobank.org/MycoTaxo.aspx?Link=T&Rec=105488"/>
<rdfs:seeAlso rdf:resource="http://www.indexfungorum.org/Names/namesrecord.asp?RecordId=105488"/>
<rdfs:seeAlso rdf:resource="http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=194551"/>
<rdfs:seeAlso rdf:resource="http://www.mycobank.org/MycoTaxo.aspx?Link=T&Rec=341143"/>
</tc:TaxonConcept>
</rdf:RDF>

Note the tc:hasName link to urn:lsid:indexfungorum.org:names:105488.

All a bit crude. The NCBI lookup is live (i.e., it's not served from a local copy of the database). I'll look at fixing this at some point, as well as caching the linkout lookups (one advantage of the live query is you can get the three dates (created, modified, and published). But for now it's a starting point to start to play with SPARQL queries across NCBI taxonomy, Index Fungorum, and IPNI using a common vocabulary.