Abstract
Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.
Highlights
There is sufficient evidence that genomic organization, whereby protein complexes contribute to the formation of long-range physical contacts between distal regulatory elements, plays an important role in dictating gene expression patterns [1, 2]
While this difference has been taken into account by some computational pipelines [28], a full characterization of the differences and similarities that exist between HiChIP and ChIA-Paired-End Tags (PETs) is lacking
58.9% of HiChIP peaks overlapped with Mbol restriction enzyme sites (Fig. 1D-F), which was much greater than the 22.9% observed for ChIA-PET
Summary
There is sufficient evidence that genomic organization, whereby protein complexes contribute to the formation of long-range physical contacts between distal regulatory elements, plays an important role in dictating gene expression patterns [1, 2]. The development of chromosome conformation capture (3C)-based technologies [4,5,6,7,8,9,10] makes it possible to detect such long-range genomic interactions at high resolution. These technologies have uncovered new principles of genome organization, including the discovery of topologically associated domains (TADs) or contact domains [11, 12], genome compartments, and interactions that physically link the regulatory elements of the genome [13], like enhancer-promoter interactions [14,15,16]. Robust and efficient computational methods are required to remove the biases associated with these molecular protocols and more accurately quantify chromatin contacts
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