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GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data
Borbala Mifsud1,2, Inigo Martincorena1, Elodie Darbo1
1The Francis Crick Institute, London, United Kingdom.
Plos One
|April 6, 2017
Summary
We developed GOTHiC, a simple binomial model to accurately identify true DNA interactions from Hi-C sequencing data. This method corrects biases and provides significance thresholds for reliable analysis.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Hi-C experiments reveal DNA spatial organization in the nucleus.
- Raw Hi-C data contains significant biases and spurious contacts, complicating true interaction identification.
Purpose of the Study:
- To introduce a novel, simple binomial probabilistic model for accurate Hi-C interaction analysis.
- To address limitations of existing methods by providing a significance threshold and resolving complex biases.
Main Methods:
- Developed a binomial probabilistic model to correct for known and unknown biases in Hi-C data.
- Implemented a p-value calculation for each interaction to establish a significance threshold.
- Validated the method against a random ligation dataset.
Main Results:
- The GOTHiC model effectively resolves complex biases in Hi-C data.
- It accurately distinguishes true DNA interactions from false positives.
- Experimental validation demonstrated superior performance compared to previous methods.
Conclusions:
- GOTHiC offers a statistically robust framework for analyzing Hi-C data.
- The method provides reliable significance thresholds for identifying true DNA interactions.
- It facilitates comparative Hi-C analyses across different experimental conditions.