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Sunday, October 15, 2017

Two conference proceedings: nanopublications and Scholia


The nanopublication conference article in
Scholia.
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.

Nanopublications
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.

Scholia
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. arXiv.org; 2017. Available from: http://arxiv.org/abs/1703.04222.

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.