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Integrative analysis of genomic, functional and protein interaction data predicts long-range enhancer-target gene interactions.

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50497

Rödelsperger,  C.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50389

Kolanczyk,  M.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

Schulz,  M. H.
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50496

Robinson,  P. N.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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nar.gkq1081.full.pdf
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Citation

Rödelsperger, C., Guo, G., Kolanczyk, M., Pletschacher, A., Köhler, S., Bauer, S., et al. (2010). Integrative analysis of genomic, functional and protein interaction data predicts long-range enhancer-target gene interactions. Nucleic Acids Research, 24(2), 1-11. doi:10.1093/nar/gkq1081.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-79E8-D
Abstract
Multicellular organismal development is controlled by a complex network of transcription factors, promoters and enhancers. Although reliable computational and experimental methods exist for enhancer detection, prediction of their target genes remains a major challenge. On the basis of available literature and ChIP-seq and ChIP-chip data for enhanceosome factor p300 and the transcriptional regulator Gli3, we found that genomic proximity and conserved synteny predict target genes with a relatively low recall of 12-27% within 2 Mb intervals centered at the enhancers. Here, we show that functional similarities between enhancer binding proteins and their transcriptional targets and proximity in the protein-protein interactome improve prediction of target genes. We used all four features to train random forest classifiers that predict target genes with a recall of 58% in 2 Mb intervals that may contain dozens of genes, representing a better than two-fold improvement over the performance of prediction based on single features alone. Genome-wide ChIP data is still relatively poorly understood, and it remains difficult to assign biological significance to binding events. Our study represents a first step in integrating various genomic features in order to elucidate the genomic network of long-range regulatory interactions.