Getting the GO into a Graph Data Structure

Today while working on a project I needed to get access to the Gene Ontology hierarchy. While there a number of GO browsers such as Amigo, I needed access to the raw data to generate a graph that I could then slice and dice. A few minutes with Python led to a simple solution.

The program parses the OBO 1.2 formatted GO data file (either by directly downloading it or from a local file) and outputs a flat dictionary listing the term ID’s, names, namespace etc and a network representation of the GO hierarchy in ncol format. It uses a simple  (and relatively non-robust) class to represent the data as an undirected graph (not really correct), though it’d be easy to use something like igraph to start doing some real network analysis. It’s certainly not a comprehensive solution, but I thought I’d put it out there.

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Deploying Predictive Models

Over the past few days I’ve been developing some predictive models in R, for the solubility data being generated as part of the ONS Solubility Challenge. As I develop the models I put up a brief summary of the results on the wiki. In the end however, we’d like to use these models to predict the solubility of untested compounds. While anybody can send me a SMILES string and get back a prediction, it’s more useful (and less work for me!) if a user can do it themselves. This requires that the models be deployed and made available as a web page or a service. Last year I developed a series of statistical web services based on R. The services were written in Java and are described in this paper. Since I’m working more with REST services these days, I wanted to see how easy it’d be to develop a model deployment system using Python, thus avoiding a multi-tiered system. With the help of rpy2, it turns out that this wasn’t very difficult.

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Update to the REST Descriptor Services

The current version of the REST interface to the CDK descriptors allowed one to access descriptor values for a SMILES string by simply appending it to an URL, resulting in something like

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/
org.openscience.cdk.qsar.descriptors.molecular.ALOGPDescriptor/c1ccccc1COCC

This type of URL is pretty handy to construct by hand. However, as Pat Walters pointed out in the comments to that post, SMILES containing ‘#’ will cause problems since that character is a URL fragment identifier. Furthermore, the presence of a ‘/’ in a SMILES string necessitates some processing in the service to recognize it as part of the SMILES, rather than a URL path separator. While the service could handle these (at the expense of messy code) it turned out that there were subtle bugs.

Based on Pats’ suggestion I converted the service to use base64 encoded SMILES, which let me simplify the code and remove the bugs. As a result, one cannot append the SMILES directly to the URL’s. Instead the above URL would be rewritten in the form

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/
org.openscience.cdk.qsar.descriptors.molecular.ALOGPDescriptor/YzFjY2NjYzFDT0ND

All the example URL’s described in my previous post that involve SMILES strings, should be rewritten using base64 encoded SMILES. So to get a document listing all descriptors for “c1ccccc1COCC” one would write

http://rguha.ath.cx/~rguha/cicc/rest/desc/descriptors/YzFjY2NjYzFDT0ND

and then follow the links therein.

While this makes it a little harder to directly write out these URL’s by hand, I expect that most uses of this service would be programmatic – in which case getting base64 encoded SMILES is trivial.

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Quick Comments on an Analysis of Antithrombotics

Joerg has made a nice blog post on the use of Open Source software and data to analyse the occurence of antithrombotics. More specifically he was trying to answer the question

Which XRay ligands are closest to the Fontaine et al. structure-activity relationship data for allowing structure-based drug design?

using Blue Obelisk tools and ChemSpider and where Fontaine et al. refers to the Fontaine Factor Xa dataset. You should read his post for a nice analysis of the problem. I just wanted to consider two points he had raised.

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Extending the REST PCA Service

I recently described a REST based service for performing PCA-based visualization of chemical spaces. By visiting a URL of the form

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/default/
c1ccccc1,c1ccccc1CC,c1ccccc1CCC,C(=O)C(=O),CC(=O)O

one would get a HTML, plain text or JSON page containing the first two principal components for the molecules specified. With this data one can generate a simple 2D plot of the distributions of molecules in the “default” chemical space.

However, as Andrew Lang pointed out on FriendFeed, one could use SecondLife to look at 3D versions of the PCA results. So I updatesd the service to allow one to specify the number of components in the URL. The above form of the service will still work – you get the first two components by default.

To specify more components use an URL of the form

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/default/3/mol1,mol2,mol3

where mol1, mol2, mol3 etc should be valid SMILES strings. The above URL will return the first three PC’s. To get just the first PC, replace the 3 with 1 and so on. If more components are requested than available, all components are returned.

Currently, the only available space is the “default” space which is 4-dimensional, so you can get a maximum of four components. In general, visit the URL

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/

to obtain a list of currently available chemical spaces, their names and dimensionality.

Caveat

While it’s easy to get all the components and visualize them, it doesn’t always make sense to do so. In general, one should consider those initial principal components that explain a significant portion of the variance (see Kaisers criterion). The service currently doesn’t provide the eigenvalues, so it’s not really possible to decide whether to go to 3, 4 or more components. For most cases, just looking at the first two principal components will sufficient – especially given the currently available chemical space.

Update (Jan 13, 2009)

Since the descriptor service now requires that Base64 encoded SMILES, the example usage URL is now invalid. Instead, the SMILES should be replaced by their encoded versions. In other words the first URL above becomes

http://rguha.ath.cx/~rguha/cicc/rest/chemspace/default/
YzFjY2NjYzE=,YzFjY2NjYzFDQw==,YzFjY2NjYzFDQ0M=,
Qyg9TylDKD1PKQ==,Q0MoPU8pTw==

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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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Getting a CAS Number from a PubChem CID

A few days back, Hari on FriendFeed had asked how one could get a a CAS number from a PubChem compound ID (CID). The reverse, that is finding a CID for a given CAS number is generally quite easy as shown by Rich here and here. Since I was trying to get some writing done, this was a good excuse for a quick hack to solve the problem.

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Multi-threaded Database Access with Python

Pub3D contains about 17.3 million 3D structures for PubChem compounds, stored in a Postgres database. One of the things we wanted to do was 3D similarity searching and to achieve that we’ve been employing the Ballester and Graham-Richards method. In this post I’m going to talk about performance – how we went from a single monolithic database with long query times, to multiple databases and significantly faster  multi-threaded queries.

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