Detecting differential peaks in ChIP-seq signals with ODIN
Manuel Allhoff1, Kristin Seré2, Heike Chauvistré2
1IZKF Computational Biology Research Group, RWTH Aachen University Medical School, Germany, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, Germany, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Germany, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Germany and Center of Informatics, Federal University of Pernambuco, Brazil IZKF Computational Biology Research Group, RWTH Aachen University Medical School, Germany, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, Germany, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Germany, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Germany and Center of Informatics, Federal University of Pernambuco, Brazil IZKF Computational Biology Research Group, RWTH Aachen University Medical School, Germany, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, Germany, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Germany, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Germany and Center of Informatics, Federal University of Pernambuco, Brazil IZKF Computational Biology Research Group, RWTH Aachen University Medical School, Germany, Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, Germany, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Germany, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Germany and Center of Informatics, Federal University of Pernambuco, Brazil.
We introduce ODIN, a novel Hidden Markov Model approach for detecting differential peaks in ChIP-seq data. ODIN accurately identifies changes in DNA-protein interactions, outperforming existing methods in various biological scenarios.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Analyzing deoxyribonucleic acid (DNA)-protein interactions via ChIP-seq is vital for understanding biological regulation.
- Differential peak calling (DPC) identifies genomic regions with altered ChIP-seq signals between conditions.
- Current methods often analyze signals individually or lack detailed interaction change detection.
Purpose of the Study:
- To develop an integrated approach for detecting and analyzing differential peaks (DPs) in paired ChIP-seq data.
- To introduce a novel Hidden Markov Model-based tool named ODIN.
- To establish an evaluation methodology for comparing DPC methods.
Main Methods:
- ODIN employs a Hidden Markov Model for integrated genomic signal processing, peak calling, and p-value calculation.
- A novel evaluation framework associates DPs with gene expression changes.
- Comparative analysis against existing DPC methods using diverse ChIP-seq datasets and simulations.
Main Results:
- ODIN successfully detects and analyzes differential peaks in ChIP-seq data within a unified framework.
- The proposed evaluation methodology effectively assesses DPC performance.
- Empirical studies demonstrate ODIN's superior performance compared to competing methods across various transcription factors, histone modifications, and simulated data.
Conclusions:
- ODIN provides a robust and integrated solution for differential peak calling in ChIP-seq analysis.
- The developed evaluation methodology offers a reliable benchmark for DPC tools.
- ODIN enhances the ability to detect subtle changes in protein-DNA interactions, advancing regulatory network analysis.
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