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Sunday, November 12, 2017

New paper: "WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research"


Focus on metabolic pathways increases the
number of annotated metabolites, further improving
the usability in metabolomics. Image: CC-BY.
TL;DR: the WikiPathways project (many developers in the USA and Europe, contributors from around the world, and many people curating content, etc) has published a new paper (doi:10.1093/nar/gkx1064/4612963), with a slight focus on metabolism. 

Full story
Almost six years ago my family and I moved back to The Netherlands for personal reasons. Workwise, I had a great time in Stockholm and Uppsala (two wonderful universities; thanks to Ola Spjuth, Bengt Fadeel, and Roland Grafström), but being immigrant in another country is not easy, not even for a western immigrant in a western country. ("There is evil among us.")

We had decided to return to our home country, The Netherlands. By sheer coincidence, I spoke with Chris Evelo in the week directly following that weekend. I had visited his group in March that year, while attending a COST-action about NanoQSAR in Maastricht. I had never been to Maastricht University yet, and this group, with their Open Source and Open Data projects, particularly WikiPathways, would give us enough to talk about. Chris had a position on the Open PHACTS project open. I was interested, applied, and ended up in the European WikiPathways group led by Martina Kutmon (the USA node is the group of Alex Pico).

Fast forward to now. It was clear to me that biological text book knowledge was unusable for any kind of computation or machine learning. It was hidden, wrongly represented, and horribly badly annotated. In fact, it still is a total mess. WikiPathways offered machine readable text book knowledge. Just what I needed to link the chemical and biological worlds. The more accurate biological annotation we put in these pathways, or semantically link to these pathways, the more precise our knowledge becomes and the better computational approaches can find and learn patterns not obvious to the human eye (it goes both ways, of course! Just read my PhD thesis.)

Over the past 5-6 years I got more and more involved in the project. Our Open PHACTS tasks did involve WikiPathways RDF (doi:10.1371/journal.pcbi.1004989), but Andra Waagmeester (now Micelio) was the lead on that. I focused on the Identifier Mapping Service, based on BridgeDb (together with great work from Carole Goble's lab, e.g. Alasdair and Christian). I focused on metabolomics.

Metabolomics
Indeed, there was plenty to be done in terms of metabolic pathways in WikiPathways. The current database had a strong focus on the genetics and proteins aspects of the pathways. In fact, many metabolites were not datanodes and therefore did not have identifiers. And without identifiers, we cannot map metabolomics data to these pathways. I started working on improving these pathways, and we did some projects using it for metabolomics data (e.g. a DTL Hotel Call project led by Lars Eijssen).

The point of this long introductions is, I am standing on the shoulders of giants. The top right figure shows, besides WikiPathways itself, and the people I just mentioned, more giants. This includes Wikidata, which we previously envisioned as hub of metabolite information (see our Enabling Open Science: Wikidata for Research (Wiki4R) proposal). Wikidata allows me to solve the problem that CAS registry numbers are hard to link to chemical structures (SMILES): it has some 70 thousand CAS numbers.


SPARQL query that lists all CAS registry numbers in Wikidata, along with the matching
SMILES (canonical and isomeric), database entry, and name of the compound. Try it.
A lot more about CAS registry numbers is found in my blog.
Finally, but certainly not least, is Denise Slenter, who started this spring in our group. She picked up things I and others were doing very quickly (for example this great work from Maastricht Science Programme students), gave those her own twist, and is now leading the practical work in taking this to the next level. This new WikiPathways paper shows the fruits of her work.

Metabolism
Of course, there are plenty of other pathways database. KEGG is still the gold standard for many. And there is the great work of Reactome, RECON, and many others (see references in the NAR article). Not to mention the important resources that integrate pathways resources. To me, unique strengths of WikiPathways include the community approach, very liberal licence (CCZero), many collaborations (do we have a slide on that?), and, importantly, its expressiveness. The latter allows our group to do the systems biology work that we do, analyzing microRNA/RNASeq data, studying diseases at a molecular interaction level, see the effects of personal genetics (SNPs, GWAS), and visually integrate and summarize the combination of experimental data and text book knowledge.

OK, this post is now already long enough. And seeing from the length, you can see how much I am impressed with WikiPathways and where it goes. Clearly, there is still a lot left to do. And I am just another person contributing to the project and honored that we could give this WikiPathways paper a metabolomics spin. HT to Alex, Tina, and Chris for that!

Slenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., Mélius, J., Cirillo, E., Coort, S. L., Digles, D., Ehrhart, F., Giesbertz, P., Kalafati, M., Martens, M., Miller, R., Nishida, K., Rieswijk, L., Waagmeester, A., Eijssen, L. M. T., Evelo, C. T., Pico, A. R., Willighagen, E. L., Nov. 2017. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research. http://dx.doi.org/10.1093/nar/gkx1064