Eduardo G Gusmao1, Christoph Dieterich1, Martin Zenke2, Ivan G Costa3. 1. IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, Helmholtz Institute for Biomedical Engineering, 52074, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil. 2. IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, Helmholtz Institute for Biomedical Engineering, 52074, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, Helmholtz Institute for Biomedical Engineering, 52074, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil. 3. IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, Helmholtz Institute for Biomedical Engineering, 52074, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, Helmholtz Institute for Biomedical Engineering, 52074, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, Helmholtz Institute for Biomedical Engineering, 52074, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil.
Abstract
MOTIVATION: The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction. RESULTS: We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such as de novo motif finding. AVAILABILITY AND IMPLEMENTATION: Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm. CONTACT: ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction. RESULTS: We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such as de novo motif finding. AVAILABILITY AND IMPLEMENTATION: Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm. CONTACT: ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Marinka Zitnik; Francis Nguyen; Bo Wang; Jure Leskovec; Anna Goldenberg; Michael M Hoffman Journal: Inf Fusion Date: 2018-09-21 Impact factor: 12.975