## Sunday, December 27, 2015

### The quality of SMILES strings in Wikidata

 Russian Wikipedia on tungsten hexacarbonyl.
One thing that machine readability adds, is all sorts of machine processing. Validation of data consistency is one. For SMILES strings, one of the things you can do is test of the string parses at all. Wikidata is machine readable, and, in fact, easier to parse than Wikipedia, for which the SMILES strings were validated recently in a J. Cheminformatics paper by Ertl et al. (doi:10.1186/s13321-015-0061-y).

Because I was wondering about the quality of the SMILES strings (and because people ask me about these things), I made some time today to run a test:
1. SPARQL for all SMILES strings
2. process each one of them with the CDK SMILES parser
I can do both easily in Bioclipse with an integrated script:

identifier = "P233" // SMILES
type = "smiles"

sparql = """
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?compound ?smiles WHERE {
?compound wdt:P233 ?smiles .
}
"""
mappings = rdf.sparqlRemote("https://query.wikidata.org/sparql", sparql)

if (ui.fileExists(outFilename)) ui.remove(outFilename)
fileContent = ""
for (i=1; i<=mappings.rowCount; i++) {
try {
wdID = mappings.get(i, "compound")
smiles = mappings.get(i, "smiles")
mol = cdk.fromSMILES(smiles)
} catch (Throwable exception) {
fileContent += (wdID + "," + smiles + ": " +

exception.message + "\n")
}
if (i % 1000 == 0) js.say("" + i)
}
ui.append(outFilename, fileContent)
ui.open(outFilename)

It turns out that out of the more than 16 thousand SMILES strings in Wikidata, only 42 could not be parsed. That does not mean they are correct, but it does mean the are wrong. Many of them turned out to be imported from the Russian Wikipedia, which is nice, as it gives me the opportunite to work in that Wikipedia instance too :)

At this moment, some 19 SMILES still need fixing (the list will chance over time, so by the time you read this...):

## Tuesday, December 22, 2015

### New Edition! Getting CAS registry numbers out of WikiData

 Source: Wikipedia. CC-BY-SA
doi:10.15200/winn.145228.82018

April this year I blogged about an important SPARQL query for many chemists: getting CAS registry numbers from Wikidata. This is relevant for two reasons:
1. CAS works together with Wikimedia on a large, free CAS-to-structure database
2. Wikidata is CCZero
The original effort validated about eight thousand registry numbers, made available via Wikipedia and the Common Chemistry website. However, the effort did not stop there, and Wikipedia now contains many more CAS registry numbers. In fact, Wikidata picked up many of these and now lists almost twenty thousand CAS numbers. That well exceeds what databases are allowed to aggregate and make available.

Since the post in April, Wikidata put online a new SPARQL end point and created "direct" property links. This way, you loose the provenance information, but the query becomes simpler:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?compound ?id WHERE {
?compound wdt:P231 ?id .
}
The other thing that changed since April is that others and I requested the creation of more compound identifiers, and here's an overview along with the current number of such identifiers in Wikidata:
Clearly, some identifiers are not well populated yet. This is what bots are for, like those used by the Andrew Su team.

Because there is also a predicate for SMILES, we can also create a query that puts the CAS registry number alongside to the SMILES (or any other identifier):
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?compound ?id ?smiles WHERE {
?compound wdt:P231 ?id ;
wdt:P233 ?smiles .
}
Of course, then the question is, are these SMILES string valid...And, importantly, this is nothing compared to the number of chemical compounds we know about, which currently is in the order of 100 million, of which a quarter can be readily purchased:

Willighagen, E., 2015. Getting CAS registry numbers out of WikiData. The Winnower.

### Using the WikiPathways API in R

 Colored pathways created with the new R package.
Earlier this week there was a question on the WikiPathways mailing list about the webservices. There are older SOAP webservices and newer REST-like webservices, which come with this nice Swagger webfront set up by Nuno. Of course, both approaches are pretty standard and you can use them from basically any environment. Still, some personas prefer to not see technical issues: "why should I know how an car engine works". I do not think any scholar is allowed you use this argument, but alas...

Of course, hiding those details is not so much of an issue, and since I have made so many R packages in the past, I decided to pick up the request to create an R package for WikiPathways: rWikiPathways. It is not feature complete yet, and not extensively tested in daily use yet (there is a test suite). But here are some code examples. Listing pathways and organisms in the wiki is done with:
organisms = listOrganisms()
pathways = listPathways()
humanPathways = listPathways(organism="Homo sapiens")

For the technology oriented users, for each pathway, you have access to the GPML source file for each pathway:
gpml = getPathway(pathway="WP4")
gpml = getPathway(pathway="WP4", revision=83654)
However, most use will likely be via database identifiers for genes, proteins, and metabolites, called Xrefs (also check out the R package for BridgeDb):
xrefs = getXrefList(pathway="WP2338", systemCode="S")
pathways = findPathwaysByXref("HMDB00001", "Ch")
pathways = findPathwaysByXref(identifier="HMDB00001", systemCode="Ch")
pathways = findPathwaysByXref(
identifier=c("HMDB00001", "HMDB00002"),
systemCode=c("Ch", "Ch") 
)
Of course, these are just the basics, and the question was about colored pathways. The SOAP code was a bit more elaborate, and this is the version with this new package (the result at the top of this post):
svg = getColoredPathway(pathway="WP1842", graphId=c("dd68a","a2c17"),
color=c("FF0000", "00FF00"));
writeLines(svg, "pathway.svg")
If you use this package in your research, please cite the below WikiPathways paper. If you have feature requests, please post the in the issue tracker.

Kutmon, M., Riutta, A., Nunes, N., Hanspers, K., Willighagen, E. L., Bohler, A., Mélius, J., Waagmeester, A., Sinha, S. R., Miller, R., Coort, S. L., Cirillo, E., Smeets, B., Evelo, C. T., Pico, A. R., Oct. 2015. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Research.

## Sunday, December 13, 2015

### SWAT4LS in Cambridge

 Wordle of the #swat4ls tweets.
Last week the BiGCaT team were present with three person (Linda, Ryan, and me) at the Sematic Web Applications and Tools 4 Life Sciences meeting in Cambridge (#swat4ls). It's a great meeting, particularly because if the workshops and hackathon. Previously, I attended the meeting in Amsterdam (gave this presentation) and Paris (which I apparently did not blog about).

Workshops
I have mixed feelings about missing half of the workshops on Monday for a visit of one of our Open PHACTS partners, but do not regret that meeting at all; I just wish I could have done both. During the visit we spoke particularly about WikiPathways and our collaboration in this area.

The Monday morning workshops were cool. First, Evan Bolton and Gang Fu gave an overview of their PubChemRDF work. I have been involved in that in the past, and I greatly enjoyed seeing the progress they have made, and a rich overview of the 250GB of data they make available on their FTP side (sorry, the rights info has not gotten any clearer over the years, but generally considered "open"). The RDF now covers, for example, the biosystems module too, so that I can query PubChem for all compounds in WikiPathways (and compare that against internal efforts).

The second workshop I attended was by Andra and others about Wikidata. The room, about 50 people, all started editing Wikidata, in trade of a chocolate letter:

The editing was about prevalence is two diseases. Both topics continued during the hackathon, see below. Slides of this presentation are online. But I missed the DisGeNET workshop, unfortunately :(

Conference
The conference itself (in the new part of Clare College, even the conference dinner) started on the second day, and all presentations are backed by a paper, linked from the program. Not having attended a semantic web conference in the past 2~ish years, it was nice to see the progress in the field. Some papers I found interesting:
But the rest is most worthwhile checking out too! The Webulous I as able to get going with some help (not paying enough attention to the GUI) for eNanoMapper:

 A Google Spreadsheet where I restricted the content of a set of cells to only subclasses of the "nanomaterial" class in the eNanoMapper ontology (see doi:10.1186/s13326-015-0005-5).
The conference ended with a panel discussion, and despite our efforts of me and the other panel members (Frank Gibson – Royal Society of Chemistry, Harold Solbrig – Mayo Clinic, Jun Zhao, University of Oxford), it took long before the conference audience really started joining in. Partly this was because the conference organization asked the community for questions, and the questions clearly did not resonate with the audience. It was not until we started discussing publishing that it became more lively. My point there was I believe the semantic web applications and tools are not really a rate limiting factor anymore, and if we really want to make a difference, we really must start changing the publishing industry. This has been said by me and others for many years already, but the pace at which things change it too low. Someone mentioned a chicken-and-egg situation, but I really believe it is all just a choice we make and an easy solution: pick up a knife, kill the chicken, and have a nice dinner. It is annoying to see all the great efforts at this conference, but much of it limited because our writing style makes nice stories and yields few machine readable facts.

Hackathon
The hackathon was held at the EBI in Hinxton (south/elixir building) and during the meeting I had a hard time deciding what to hack on: there just were too many interesting technologies to work on, but I ended up working on PubChem/HDT (long) and Wikidata (short). The timings are based on the amount of help I needed to bootstrap things and how much I can figure out at home (which is a lot for Wikidata).

HDT (header, dictionary, triple) is a not-so-new-but-under-the-radar technology for binary storing triples in a file based store. The specification outlines this binary format as well as the index. That means that you can share triple data compressed and indexed. That opens up new possibilities. One thing I am interested in, is using this approach for sharing link sets (doi:10.1007/978-3-319-11964-9_7) for BridgeDb, our identifier mapping platform. But there is much more, of course: share life science databases on your laptop.

This hack was triggered by a beer with Evan Bolton and Arto Bendiken. Yes, there is a Java library, hdt-java, and for me the easiest way to work out how to use a Java API, is to write a Bioclipse plugin. Writing the plugin is trivial, though setting up a Bioclipse development is less so: the New Wizard does the hard work in seconds. But then started the dependency hacking. The Jena version it depended on is incompatible with the version in Bioclipse right now, but that is not a big deal for Eclipse, and the outcome is that we have both version on the classpath :) That, however, did require me to introduce a new plugin, net.bioclipse.rdf.core with the IRDFStore, something I wanted to do for a long time, because that is also needed if one wants to use Sesame/OpenRDF instead of Jena.

So, after lunch I was done with the code cleanup, and I got to the HDT manager again. Soon, I could open a HDT file. I first had the API method to read it into memory, but that's not what I wanted, because I want to open large HDT files. Because it uses Jena, it conveniently provides a Jena Model object, so adding SPARQL-ing support was easy; I cannot use the old SPARQL-ing code, because then I would start mixing Jena versions, but since all is Open Source, I just copied/pasted the code (which is written by me in the first place, doi:10.1186/2041-1480-2-s1-s6, interestingly, work that originates from my previous SWAT4LS talk :). Then, I could do this:
It is file based, which has different from a full triple store server. So, questions arise about performance. Creating an index takes time and memory (1GB of heap space, for example). However, the index file can be shared (downloaded) and then a HDT file "opens" in a second in Bioclipse. Of course, the opening does not do anything special, like loading into memory, and should be compared to connecting to a relational database. The querying is what takes the time. Here are some numbers for the Wiktionary data that the RDFHDT team provides as example data set:
However, I am not entirely sure what to compare this against. I will have to experiment with, for example, ChEMBL-RDF (maybe update the Uppsala version, see doi:10.1186/1758-2946-5-23). The advantage would be that ChEMBL data could easily be distributed along with Bioclipse to service the decision support features. Because the typical query is asking for data for a parcicular compound, not all compounds. If that works within less than 0.1 seconds, then this may give a nice user experience.

But before I reach that, it needs a bit more hacking:
1. take the approach I took with BridgeDb mapping databases for sharing HDT files (which has the advantage that you get a decent automatic updating system, etc)
2. ensure I can query over more than one HDT file
And probably a bit more.

Wikidata and WikiPathways
After the coffee break I joined the Wikidata people, and sat down to learn about the bots. However, Andra wanted to finish something else first, where I could help out. Considering I probably manage to hack up a bot anyway, we worked on the following. Multiple database about genes, proteins, and metabolites like to link these biological entities to pathways in WikiPathways (doi:10.1093/nar/gkv1024). Of course, we love to collaborate with all the projects that integrate WikiPathways into their systems, but I personally rather use a solution that services all needs. If only because then people can do this integration without needing our time. Of course, this is an idea we pitched about a year ago in the Enabling Open Science: WikiData for Research proposal (doi:10.5281/zenodo.13906).

That is, would it not be nice of people can just pulled the links between the biological entities to WikiPathways from Wikidata, using one of the many APIs they have (SPARQL, REST), supporting multiple formats (XML, JSON, RDF)? I think so, as you might have guessed. So does Andra, and he asked me if I could start the discussions in the Wikidata community, which I happily did. I'm not sure about the outcome, because despite having links like these is not of their prime interest - they did not like the idea of links to the Crystallography Open Database much yet, with the argument it is a one-to-many relation - though this is exactly what the PDB identifier is too, and that is accepted. So, it's a matter of notability again. But this is what the current proposal looks like:

Let's see how the discussion unfolds. Please feel tree to coin in and show your support, comments, questions, or opposition, so that we can together get this right.

Chemistry Development Kit
There is undoubtedly a lot more, but I have been summarizing the meeting for about three hours now, getting notes together etc. A last thing I want to mention now, is the CDK. Cheminformatics is, afterall, a critical feature of life science data, and spoke with a few about the CDK. And I visited NextMove Software on Friday where John May works nowadays, who did a lot of work on the CDK recently (we also spoke about WikiPathways and eNanoMapper). NextMove is doing great stuff (thanks for the invitation), and so did John during his PhD in Chris Steinbeck's group at the EBI. But during the conference I also spoke with others about the CDK and following up on these conversations.