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Wordle of the #swat4ls tweets. |
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:
— Egon Willighagen (@egonwillighagen) December 7, 2015
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:
- Wikidata: A platform for data integration and dissemination for the life sciences and beyond
- OBOPedia: An encyclopaedia of biology using OBO ontologies
- A new Ontology Lookup Service at EMBL-EBI
- Collaborative Ontology Development Using the Webulous Architecture and Google App
- Customizing “General SPARQL” for visualisation of in-house data in Cytoscape
- Validata: An online tool for testing RDF data conformance
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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). |
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:
#swat4ls #bioclipse pubchemData = hdt.createStore("/tmp/pc_biosystem.hdt"); data = hdt.sparql(pubchemData, sparql) pic.twitter.com/1yFi1Jrvrm
— Egon Willighagen (@egonwillighagen) December 10, 2015
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:
OK, so 64M triples, in 960MB file (HDT+index). All languages with SPARQL in 30 seconds. All 880k English words in 140 secs
— Egon Willighagen (@egonwillighagen) December 11, 2015
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:
- 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)
- ensure I can query over more than one HDT file
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.
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