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PASTAA: identifying transcription factors associated with sets of co-regulated genes.

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50500

Roider,  Helge G.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50420

Manke,  Thomas
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

O'Keeffe,  Sean
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50613

Vingron,  Martin
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

Haas,  Stefan A.
Max Planck Society;

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435.pdf
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Citation

Roider, H. G., Manke, T., O'Keeffe, S., Vingron, M., & Haas, S. A. (2009). PASTAA: identifying transcription factors associated with sets of co-regulated genes. Bioinformatics, 25(4), 435-442. doi:10.1093/bioinforma10.1093/bioinformatics/btn627.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-7DBF-D
Abstract
Motivation A major challenge in regulatory genomics is the identification of associations between functional categories of genes (e.g. tissues, metabolic pathways) and their regulating transcription factors (TFs). While, for a limited number of categories, the regulating TFs are already known, still for many functional categories the responsible factors remain to be elucidated. Results We put forward a novel method (PASTAA) for detecting transcriptions factors associated with functional categories, which utilizes the prediction of binding affinities of a TF to promoters. This binding strength information is compared to the likelihood of membership of the corresponding genes in the functional category under study. Coherence between the two ranked datasets is seen as an indicator of association between a TF and the category. PASTAA is applied primarily to the determination of TFs driving tissue-specific expression. We show that PASTAA is capable of recovering many TFs acting tissue specifically and, in addition, provides novel associations so far not detected by alternative methods. The application of PASTAA to detect TFs involved in the regulation of tissue-specific gene expression revealed a remarkable number of experimentally supported associations. The validated success for various datasets implies that PASTAA can directly be applied for the detection of TFs associated with newly derived gene sets.