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Bioinformatics. 2014 Sep 1;30(17):i401-7. doi: 10.1093/bioinformatics/btu446.

Estimating the activity of transcription factors by the effect on their target genes.

Author information

1
Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Beutenbergstrasse 11a, 07745 Jena, Theoretical Bioinformatics, German Cancer Research Center, INF 580, 69121 Heidelberg, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267 and Division Translational Immunology, Group Tumor Antigens, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Beutenbergstrasse 11a, 07745 Jena, Theoretical Bioinformatics, German Cancer Research Center, INF 580, 69121 Heidelberg, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267 and Division Translational Immunology, Group Tumor Antigens, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Beutenbergstrasse 11a, 07745 Jena, Theoretical Bioinformatics, German Cancer Research Center, INF 580, 69121 Heidelberg, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267 and Division Translational Immunology, Group Tumor Antigens, German Cancer R
2
Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Beutenbergstrasse 11a, 07745 Jena, Theoretical Bioinformatics, German Cancer Research Center, INF 580, 69121 Heidelberg, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267 and Division Translational Immunology, Group Tumor Antigens, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Beutenbergstrasse 11a, 07745 Jena, Theoretical Bioinformatics, German Cancer Research Center, INF 580, 69121 Heidelberg, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267 and Division Translational Immunology, Group Tumor Antigens, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.
3
Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Beutenbergstrasse 11a, 07745 Jena, Theoretical Bioinformatics, German Cancer Research Center, INF 580, 69121 Heidelberg, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267 and Division Translational Immunology, Group Tumor Antigens, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.

Abstract

MOTIVATION:

Understanding regulation of transcription is central for elucidating cellular regulation. Several statistical and mechanistic models have come up the last couple of years explaining gene transcription levels using information of potential transcriptional regulators as transcription factors (TFs) and information from epigenetic modifications. The activity of TFs is often inferred by their transcription levels, promoter binding and epigenetic effects. However, in principle, these methods do not take hard-to-measure influences such as post-transcriptional modifications into account.

RESULTS:

For TFs, we present a novel concept circumventing this problem. We estimate the regulatory activity of TFs using their cumulative effects on their target genes. We established our model using expression data of 59 cell lines from the National Cancer Institute. The trained model was applied to an independent expression dataset of melanoma cells yielding excellent expression predictions and elucidated regulation of melanogenesis.

AVAILABILITY AND IMPLEMENTATION:

Using mixed-integer linear programming, we implemented a switch-like optimization enabling a constrained but optimal selection of TFs and optimal model selection estimating their effects. The method is generic and can also be applied to further regulators of transcription.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25161226
PMCID:
PMC4147899
DOI:
10.1093/bioinformatics/btu446
[Indexed for MEDLINE]
Free PMC Article

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