rHVDM: an R package to predict the activity and targets of a transcription factor

SUMMARY

Highly parallel genomic platforms like microarrays often present researchers with long lists of differentially expressed genes but contain little or no information on how these genes are regulated. rHVDM is a novel R package which uses gene expression time course data to predict the activity and targets of a transcription factor. In the first step, rHVDM uses a small number of known targets to derive the activity profile of a given transcription factor. Then, in a subsequent step, this activity profile is used to predict other putative targets of that transcription factor. A dynamic and mechanistic model of gene expression is at the heart of the technique. Measurement error is taken into account during the process, which allows an objective assessment of the robustness of fit and, therefore, the quality of the predictions. The package relies on efficient algorithms and vectorization to accomplish potentially time consuming tasks including optimization and differential equation integration. We demonstrate the efficiency and accuracy of rHVDM by examining the activity of the tumour-suppressing transcription factor, p53.

AVAILABILITY

The version of the package presented here (1.8.1) is freely available from the Bioconductor Web site (http://bioconductor.org/packages/2.3/bioc/html/rHVDM.html).

Reconstructing gene networks: what are the limits?

To fully realize the benefits of high-throughput post-genomic technologies it is necessary to reconstruct and analyse the complicated network of interactions through which most genes operate. We briefly summarize the mathematical frameworks that can be used to model such networks, and the types of algorithms available for their reconstruction. We then focus on dynamic models, typically described using differential equations, and explain the two main reconstruction approaches in current use. We discuss the data requirements of these algorithms and ask how well they correspond to current microarray data.