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A data integration framework for prediction of transcription factor targets.

ABSTRACT: We present a computational framework for predicting targets of transcription factor regulation. The framework is based on the integration of a number of sources of evidence, derived from DNA-sequence and gene-expression data, using a weighted sum approach. Sources of evidence are prioritized based on a training set, and their relative contributions are then optimized. The performance of the proposed framework is demonstrated in the context of BCL6 target prediction. We show that this framework is able to uncover BCL6 targets reliably when biological prior information is utilized effectively, particularly in the case of sequence analysis. The framework results in a considerable gain in performance over scores in which sequence information was not incorporated. This analysis shows that with assessment of the quality and biological relevance of the data, reliable predictions can be obtained with this computational framework.

SUBMITTER: Nykter M

PROVIDER: S-EPMC2771581 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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A data integration framework for prediction of transcription factor targets.

Nykter Matti M Lähdesmäki Harri H Rust Alistair A Thorsson Vesteinn V Shmulevich Ilya I

Annals of the New York Academy of Sciences 20090301

We present a computational framework for predicting targets of transcription factor regulation. The framework is based on the integration of a number of sources of evidence, derived from DNA-sequence and gene-expression data, using a weighted sum approach. Sources of evidence are prioritized based on a training set, and their relative contributions are then optimized. The performance of the proposed framework is demonstrated in the context of BCL6 target prediction. We show that this framework i ...[more]

PMID: 19348642

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Project description:BackgroundSystems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.ResultsIn order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.ConclusionsOur SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Dataset Information

A data integration framework for prediction of transcription factor targets.

Publications

A data integration framework for prediction of transcription factor targets.

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