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An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
Published on: April 5, 2018
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CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction
Silvia Galan1,2, Nick Machnik1,3, Kai Kruse1
1Max Planck Institute for Molecular Biomedicine, Münster, Germany.
Nature Genetics
|October 20, 2020
Summary
We developed CHESS, a new algorithm to compare 3D chromatin organization maps. This method accurately identifies differences in chromatin structure across species, developmental stages, and diseases.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Three-dimensional (3D) chromatin organization is crucial for gene regulation, replication, and development.
- Understanding dynamic changes in chromatin structure is key to deciphering evolutionary and regulatory mechanisms.
Purpose of the Study:
- To introduce CHESS (Comparison of Hi-C Experiments using Structural Similarity), a novel algorithm for comparing chromatin contact maps.
- To enable automatic differential feature extraction for analyzing chromatin organization.
Main Methods:
- CHESS algorithm development for comparing chromatin contact maps.
- Demonstration of CHESS robustness against experimental variability.
- Application of CHESS to diverse biological datasets.
Main Results:
- CHESS successfully performed interspecies comparisons of syntenic regions in human and mouse.
- Identified conformational changes in Zelda-depleted Drosophila embryos.
- Detected patient-specific aberrant chromatin conformation in diffuse large B-cell lymphoma.
- Systematically identified chromatin contact differences in high-resolution Capture-C data.
Conclusions:
- CHESS is a computationally efficient and robust method for comparing and classifying changes in chromatin contact data.
- The algorithm facilitates the study of 3D genome dynamics in various biological contexts.
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