Posts Tagged ‘network’

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 simpleĀ  (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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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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Last year, John Van Drie and I published two papers (here and here) on the Structure Activity Landscape Index, (SALI) which is a way to view SAR data as a network of compounds. Along with the paper ,I put up a simple Java application (licensed under the LGPL) to generate and explore these networks. – you only need to provide a file containing SMILES and activities. It’s based on ZGRViewer – a very slick GUI for Graphviz generated networks. I finally got around to reorganizing the code and putting it up on a GitHub repository. You can get more details of the application and the last stable version here.


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The Curious Wavefunction has a nice post on the issue of selective and non-selective kinase inhibitors. An interesting commentary, especially in the light of the recent paper on network polypharmcology. While there have been a number of papers on polypharmcology and the idea itself is very attractive, it has seemed to me that for this approach to succeed we need very detailed information on the targets and systems involved in these networks. Indeed, a current project of mine is currently hitting this problem. As Ashutosh notes,

… in the first place we don’t even know what specific subset of kinases to hit for treating a particular disease. First comes target validation, then modulation.

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