Annotating Bioassays

I’ve been working for some time with the PubChem Bioassay collection – a set of 1293 assays that cover a range of techniques (enzymatic, phenotypic etc.), targets and sizes (from 20 molecules to 200,000 molecules). In addition, some assays are primary, high-throughput assays whereas a number of them are smaller, confirmatory assays. While an extremely valuable collection, one of the drawbacks is the lack of curation. This has led to some people saying that the data is too noisy to be useful. Yes, the noise is a problem, but I think there’s still useful data to extract and model.

One of the problems that I have faced is that while one can perform a full text search for assays on PubChem, there is no form of annotations on the assays themselves. One effect of this is that it is difficult to link an assay to other biological resources (though for enzymatic assays, one can determine a Pubmed protein identifier). While working on my bioassay network project, I needed annotations and I didn’t want to do it manually.

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The ONS Challenge & Visualizing Chemical Space

The ONSChallenge has been running for some time now and the simple web query form that tied in the data from Google Docs along with web services from IU has turned out to be pretty handy. With more and more data becoming available, I had done some initial exploratory analysis of the measured solubilities. One thing that is useful to the experimentalists is a suggestion of which compound to test next. This could be made on the basis of many factors – availability, ease of synthesis and so on. But one way to look at it is to examine what types of compounds have been tested previously, and suggest that the subsequent compounds be very different from those that have been tested.

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Conformational Envelopes

Joe Leonard posted a question on the CCL mailing list today regarding “conformation envelopes”. More specifically, he asked

Has there been work on creating visualizations of “conformer envelopes”, graphical representations of the conformational space occupied (or available) to molecules. Particularly when such visualizations are used to (quickly/visually) compare whether 2 molecules can adopt the same shape – or if there are shapes of one that can’t be adopted by another.

A while back when I was investigating the use of the Ballester & Graham-Richards shape descriptors for 3D similarity searching. It turns out they perform quite poorly in enrichment benchmarks (which I’ll describe in a future post). At that time I was thinking of how Pub3D could scale to a multi-conformer version and I realized that the shape descriptors would allow me to easily visualize the “shape space” of a set of compounds. When these compounds are conformers for a molecule, one effectively gets a conformational envelope.

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Live ONS Solubility Queries

In a previous post, I described a simple web form to query and visualize the solubility data being generated as part of the ONS Challenge. The previous approach required me to manually download the data and load it into a Postgres database. While trivial from a coding point of view, it’s a pain since I have to keep my local DB in sync with the Google Docs spreadsheet.

However, Google comes to the rescue with their Query API, which allows us to view the spreadsheet as a table which can be queried using an SQL like language. As a result, I can ditch the whole local database, and simply have an HTML page constructed using Javascript, which performs queries directly on the solubility spreadsheet.

This is very nice since I now no longer have to maintain a local DB and ensure that it’s in sync with Jean-Claudes results. Of course, there are some drawbacks to this method. First, the query page will assume that the data in the spreadsheet is clean. So if there are two entries called “Ethanol” and “ethanol”, they will be considered seperate solvents. Secondly, this approach cannot be used to include cheminformatics in the queries, since Google doesn’t support that functionality. Finally, it’s not going to be very good for large spreadsheets.

However, this is a very nice API, that allows one to elegantly integrate web applications with live data. I heart Google!

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Solubility Queries and the Google Visualization API

There was a FriendFeed dicussion on the use of RDF triples for representing the solubilty data generated by Jean-Claude and others as part of the ONS Solubility Challenge. Part of the discussion revolved around letting RDF novices easily perform queries of the data being collected.  Not knowing much about RDF, I took the raw data from the Google Docs and loaded it into a Postgres database and whipped up a simple query form.

The DB and form are nothing remarkable. But what is cool is that the Google Visualization API makes it really easy for me include charts and other visualizations very easily. For example, if you select “any” as the solvent and then select a solute, the form creates a table of solubilities of that solute in all the solvents it was measured in. A natural view of the data is to look at a bar chart of the solubilities across the various solvents.

Since my form is built using mod_python, it’s a simple matter to write out the Javascript to call the Google API. After some boilerplate code, all that needs to be done is to create a DataTable object, set the column types and names and then populate it. See here for example code, which I modified.


var data = new google.visualization.DataTable();
data.addColumn(’string’, ‘Solvent’);
data.addColumn(’number’, ‘Conc (M)’);
data.addRows(5);
data.setValue(0, 0, ‘thf’);
data.setValue(0, 1, 1.23);
data.setValue(1, 0, ‘acetonitrile’);
data.setValue(1, 1, 2.34);


Once you have the data all stored, some more boilerplate code allows us to easily insert the chart into the final web page. Very neat!

(Of course, since these queries do not involve chemistry / cheminformatics, I could skip Python and Postgres and simply do the whole thing in Javascript, querying the Google Docs spreadsheet directly. This means that the results from the form would always be in sync with the Google Doc, but that’s for another evening)

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Surnames Across the Globe

I came across an interesting site called the World Names Profiler, which given a surname colors a map of the world based on frequency of occurence of the name in different countries. They have a dataset of 300 million names across 26 countries.

While it’s a nice visualization, it was very interesting for me to see the spread of Indian surnames, as the Indian diaspora is spread out all over the globe. Obviously Indian surnames have a maximum frequency in India, but it’s quite interesting to note that Guha has a high frequency in North America and Central Europe and a very low frequency in Australia. I was also surprised to see that it had a non-zero occurrence in Argentina. On the other hand, Ghosh, is has a higher frequency in Canada compared to the US and a higher frequency in Argentina than Guha. However, Patel, has a much higher frequency in Australia than either Guha or Ghosh. Singh on the other hand, appears to have similar frequencies in Canada and Australia, which are both higher than in the US

I chose these surname because they’re pretty common Indian surnames. One could correlate frequencies of occurrence to the background represented by the surnames, but that would be easily confounded by stereotypes. However, for me, it’s a nice visualization of how Indians have spread over the globe.

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