Sunday, October 15, 2017

Two conference proceedings: nanopublications and Scholia

The nanopublication conference article in
It takes effort to move scholarly publishing forward. And the traditional publishers have not all shown to be good at that: we're still basically stuck with machine-broken channels like PDFs and ReadCubes. They seem to all love text mining, but only if they can do it themselves.

Fortunately, there are plenty of people who do like to make a difference and like to innovate. I find this important, because if we do not do it, who will. Two people who make an effort are two researchers who recently published their work as conference proceedings: Tobias Kuhn and Finn Nielsen. And I am happy to have been able to contribute to both efforts.

Tobias works on nanopublications which innovates how we make knowledge machine readable. And I have stressed how important this is in my blog for years. Nanopublications describe how knowledge is captures, makes it FAIR, but importantly, it links the knowledge to the research that led to the knowledge. His recent conference proceedings details how nanopublications can be used to establish incremental knowledge. That is, given two sets of nanopubblications, it determines which have been removed, added, and changed. The paper continues outlining how that can be used to reduce, for example, download sizes and how it can help establish an efficient change history.

And Finn developed Scholia, an interface not unlike Web-of-Science. But then based on Wikidata and therefore fully on CCZero data. And, with a community actively adding the full history of scholarly literature and the citations between papers, courtesy to the Initiative for Open Citations. This is opening up a lot of possibilities: from keeping track of articles citing your work, to get alerts of articles publishing new data on your favorite gene or metabolite.

Kuhn T, Willighagen E, Evelo C, Queralt-Rosinach N, Centeno E, Furlong L. Reliable Granular References to Changing Linked Data. In: d'Amato C, Fernandez M, Tamma V, Lecue F, Cudré-Mauroux P, Sequeda J, et al., editors. The Semantic Web – ISWC 2017. vol. 10587 of Lecture Notes in Computer Science. Springer International Publishing; 2017. p. 436-451. doi:10.1007/978-3-319-68288-4_26

Nielsen FÃ, Mietchen D, Willighagen E. Scholia and scientometrics with Wikidata.; 2017. Available from:

Sunday, October 08, 2017

CDK used in SIRIUS 3: metabolomics tools from Germany

Screenshot from the SIRIUS 3 Documentation.
License: unknown.
It has been ages I blogged about work I heard about and think should receive more attention. So, I'll try to pick up that habit again.

After my PhD research (about machine learning (chemometrics, mostly), crystallography, QSAR) I first went into the field metabolomics. Because is combines core chemistry with the complexity biology. My first position was with Chris Steinbeck, in Cologne, within the bioinformatics institute led by Prof. Schomburg (of the BRENDA database). During that year, I worked in a group that worked on NMR data (NMRShiftDb, dr. Stefan Kuhn), Bioclipse (collaboration with Ola Spjuth), and, of course, the Chemistry Development Kit (see our new paper).

This new paper, actually, introduces functionality that was developed in that year, for example, work started by Miquel Rojas-Cheró. This includes the work on atom types, which we needed to handle radicals, lone pairs, etc, for delocalisation. It also includes work around handling molecular formula and calculating molecular formulas from (accurate) molecular masses. For the latter, more recent work even further improved on earlier work.

So, whenever metabolomics work is published and they use the CDK, I realize that what the CDK does has impact. This week Google Scholar alerted me about a user guidance document for SIRIUS 3 (see the screenshot). Seems really nice (great) work from Sebastian Böcker et al.!

It also makes me happy, as our Faculty of Heath, Medicine, and Life Sciences (FHML) is now part of the Netherlands Metabolomics Center, and that we published the recent article our vision of a stronger, more FAIR European metabolomics community.

Wednesday, October 04, 2017

new paper: "The future of metabolomics in ELIXIR"

CC-BY from F1000 article.
This spring I attended a meeting organized by researchers from the European metabolomics community, including from PhenoMeNal to talk about proposing a use case to ELIXIR. Doing research in metabolomics and being part of ELIXIR, I was happy that meeting happened. During the meeting I presented the work from our BiGCaT group (e.g. WikiPathways, see doi:10.1093/nar/gkv1024).

During the meeting various metabolomics topics were discussed, and I pushed for interoperability of chemical (metabolic) structures, which requires structure normalization, equivalence testing, etc. You know, the kind of work that partners in Open PHACTS did, and that we're now trying to bootstrap with ChemStructMaps. It did not make it, but ideas are included in the selected topic.

All this you can read in this meeting write up, peer-reviewed in F1000Research (doi:10.12688/f1000research.12342.1). I am happy to have been given the opportunity to contribute to this work. The work in our group (e.g. from our PhD student Denise) can surely contribute to this community effort.

 Van Rijswijk M, Beirnaert C, Caron C, Cascante M, Dominguez V, Dunn WB, et al. The future of metabolomics in ELIXIR. F1000Research. 2017 Sep;6:1649+. 10.12688/f1000research.12342.1.

Saturday, September 09, 2017

New paper: "RDFIO: extending Semantic MediaWiki for interoperable biomedical data management"

Figure 10 from the article showing what the DrugMet wiki
with the pKa data looked like. CC-BY.
When I was still doing research at Uppsala University, I had a internship student, Samuel Lampa, who did wonderful work on knowledge representation and logic (check his thesis). In that same period he started RDFIO, a Semantic MediaWiki extension to provide a SPARQL end point and some clever feature to import and export RDF. As I was already using RDF in my research, and wikis are great way to explore how to model domain data, particularly when extracted from diverse literature, I was quite interested. Together we worked on capturing pKa data, and Samuel had put DrugMet online. Extracting pKa values from primary literature is a lot of laborious work and crowdsourcing did not pick up. This data was migrated to Wikidata about a year ago.

I also used the RDFIO extension when I started capturing nanosafety data from literature when I worked at Karolinska Institutet. I will soon write up this work, as the NanoWiki (check out these FigShare data releases) was a seminal data set in eNanoMapper, during which I continued adding data to test new AMBIT features.

Earlier this week Samuel's write up of his RDFIO project was published, to which I contributed the pKa use case (doi:10.1186/s13326-017-0136-y). There are various ways to install the software, as described on the RDFIO project site. The DrugMet data as well as the data for the OrphaNet data from the other example use case can also be downloaded from that site.

Lampa, S., Willighagen, E., Kohonen, P., King, A., Vrandečić, D., Grafström, R., & Spjuth, O. (2017). RDFIO: extending semantic MediaWiki for interoperable biomedical data management. Journal of Biomedical Semantics, 8 (1).

Sunday, August 27, 2017

DataCite: the PubMed for data and software

We have services like PubMed, Europe PMC, and Google Scholar to make a list of literature. Scholia/Wikidata and ORCID are upcoming services, but for data and software there are fewer options. One notable exception is DataCite (two past blogs where I mentioned it). There is plenty of caution in interpreting the results, like versioning, the fact that preprints, posters, etc are also hosted by the supported repositories (e.g. Figshare, Zenodo), but it seems the faceted browsing based on metadata is really improving.

This is what my recent "DataCite" history looks like:

And it get's even more exciting when you realize that DataCite integrates with ORCID so that you can have it all listed on your ORCID profile.