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HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking.

Charalampos Lazaris^1,2, Stephen Kelly^3,4, Panagiotis Ntziachristos⁵

¹Department of Pathology, NYU School of Medicine, New York, NY, 10016, USA.

|January 7, 2017

Summary

HiC-bench is a new platform for analyzing Hi-C data, simplifying complex tasks and ensuring reproducible results in genome organization studies. It allows for easy comparison of different analysis tools and parameters.

Keywords:

Benchmarking Chromosome conformation Computational pipeline Data provenance Hi-C Parameter exploration

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Chromatin conformation capture (Hi-C) techniques offer high-resolution insights into genome organization.
Analyzing Hi-C data is computationally intensive and requires robust quality assessment.
Existing tools often lack comprehensive analysis and quality control options, leading to user uncertainty.

Purpose of the Study:

To introduce HiC-bench, a configurable platform for comprehensive and reproducible Hi-C data analysis.
To address the need for comparing, exploring, and reproducing Hi-C analysis workflows.
To facilitate the optimal use of various tools and parameters in Hi-C data analysis.

Main Methods:

HiC-bench integrates alignment, filtering, contact matrix generation, normalization, and topological domain identification.
The platform includes quality assessment and visualization tasks using established and novel methods.
It is implemented as a data flow platform emphasizing reproducibility and enabling combinatorial parameter and tool exploration.

Main Results:

HiC-bench provides a unified environment for common Hi-C analysis tasks.
The platform facilitates systematic benchmarking of analysis tools and parameters.
A comprehensive benchmark of TAD callers was performed, exploring various correction methods, parameters, and sequencing depths.

Conclusions:

HiC-bench offers an extensible and user-friendly platform for analyzing Hi-C datasets.
It is expected to streamline current analyses and support hypothesis testing in 3D genome organization.
The platform aids scientists in formulating and testing new hypotheses in the field.