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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-79E8-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-79E9-B
Genre: Journal Article
Alternative Title : Nucleic Acids Res

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 Creators:
Rödelsperger, C.1, Author              
Guo, G., Author
Kolanczyk, M.1, Author              
Pletschacher, A., Author
Köhler, S., Author
Bauer, S., Author
Schulz, M. H.2, Author
Robinson, P. N.1, Author              
Affiliations:
1Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433557              
2Max Planck Society, ou_persistent13              

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 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.

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Language(s): eng - English
 Dates: 2010-11-24
 Publication Status: Published in print
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Title: Nucleic Acids Research
  Alternative Title : Nucleic Acids Res
Source Genre: Journal
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Pages: - Volume / Issue: 24 (2) Sequence Number: - Start / End Page: 1 - 11 Identifier: ISSN: 0305-1048