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Systematic evaluation of statistical methods for identifying looping interactions in 5C data

Thomas G. Gilgenast, View ORCID ProfileJennifer E. Phillips-Cremins
doi: https://doi.org/10.1101/201681
Thomas G. Gilgenast
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
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Jennifer E. Phillips-Cremins
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
2Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
3Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104
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  • ORCID record for Jennifer E. Phillips-Cremins
  • For correspondence: jcremins@seas.upenn.edu
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Summary

Chromosome-Conformation-Capture-Carbon-Copy (5C) is a molecular technology based on proximity ligation that enables high-resolution and high-coverage inquiry of long-range chromatin looping interactions. Computational pipelines for analyzing 5C data involve a series of inter-dependent normalization procedures and statistical methods that markedly influence downstream biological results. A detailed analysis of the trade-offs inherent to all stages of 5C analysis has not been reported, but is essential for understanding the biological basis of looping. Here, we provide a comparative assessment of method performance at each step in the 5C analysis pipeline, including sequencing depth and library complexity correction, bias mitigation, spatial noise reduction, distance-dependent expected and variance estimation, modeling, and loop detection. We present a detailed discussion of methodological advantages/disadvantages at each step and provide a full suite of algorithms, lib5C, to allow investigators to test the range of approaches on their own high-resolution 5C data. Principles learned from our comparative analyses will have broad impact on many other forms of Chromosome-Conformation-Capture-based data, including Hi-C, 4C, and Capture-C.

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Posted October 11, 2017.
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Systematic evaluation of statistical methods for identifying looping interactions in 5C data
Thomas G. Gilgenast, Jennifer E. Phillips-Cremins
bioRxiv 201681; doi: https://doi.org/10.1101/201681
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Systematic evaluation of statistical methods for identifying looping interactions in 5C data
Thomas G. Gilgenast, Jennifer E. Phillips-Cremins
bioRxiv 201681; doi: https://doi.org/10.1101/201681

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