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Sunday, July 30, 2017

Wikidata visualizes SMILES strings with John Mayfield's CDK Depict


SVG depiction of D-ribulose.
Wikidata is building up a curated collection of information about chemicals. A lot of data originates from Wikipedia, but active users are augmenting this information. Of particular interest, in this respect, is Sebastian's PubChem ID curation work (he can use a few helping hands!). Followers of my blog know that I am using Wikidata as source of compound ID mapping data for BridgeDb.

Each chemical can have one or two associated SMILES strings. A canonical SMILES, that excludes any chirality, and a isomeric SMILES that does include chirality. Because statement values can be linked to a formatter URL, Wikidata often has values associated with a link. For example, for the EPA CompTox Dashboard identifiers it links to that database. Kopiersperre used this approach to link to John Mayfield's CDK Depict.

Until two weeks ago, the formatter URL for both the canonical and isomeric SMILES was he same. I changed that, so that when a isomeric SMILES is depicted, it shows the perceived R,S (CIP) annotation as well. That should help further curation of Wikidata and Wikipedia content.

Wednesday, July 05, 2017

new paper: "A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury"

Figure from the article. CC-BY.
One of the projects I worked on at Karolinska Institutet with Prof. Grafström was the idea of combining transcriptomics data with dose-response data. Because we wanted to know if there was a relation between the structures of chemicals (drugs, toxicants, etc) and how biological systems react to that. Basically, testing the whole idea behind quantitative-structure activity relationship (QSAR) modeling.

Using data from the Connectivity Map (Cmap, doi:10.1126/science.1132939) and NCI60, we set out to do just that. My role in this work was to explore the actual structure-activity relationship. The Chemistry Development Kit (doi:10.1186/s13321-017-0220-4) was used to calculate molecular descriptor, and we used various machine learning approaches to explore possible regression models. Bottom line was, it is not possible to correlate the chemical structures with the biological activities. We explored the reason and ascribe this to the high diversity of the chemical structures in the Cmap data set. In fact, they selected the chemicals in that study based on chemical diversity. All the details can be found in this new paper.

It's important to note that these findings does not validate the QSAR concept, but just that they very unfortunately selected their compounds, making exploration of this idea impossible, by design.

However, using the transcriptomics data and a method developed by Juuso Parkkinen it is able to find multivariate patterns. In fact, what we saw is more than is presented in this paper, as we have not been able to support further findings with supporting evidence yet. This paper, however, presents experimental confirmation that predictions based on this component model, coined the Predictive Toxicogenocics Gene Space, actually makes sense. Biological interpretation is presented using a variety of bioinformatics analyses. But a full mechanistic description of the components is yet to be developed. My expectation is that we will be able to link these components to key events in biological responses to exposure to toxicants.

 Kohonen, P., Parkkinen, J. A., Willighagen, E. L., Ceder, R., Wennerberg, K., Kaski, S., Grafström, R. C., Jul. 2017. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nature Communications 8. 
https://doi.org/10.1038/ncomms15932