Mustache: Multi-scale Detection of Chromatin Loops from Hi-C and Micro-C Maps using Scale-Space Representation

Abstract

We present MO_SCPLOWUSTACHEC_SCPLOW, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MO_SCPLOWUSTACHEC_SCPLOW employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in a multi-scale representation of chromatin contact maps parametrized by the size of the smoothing kernel. When applied to high-resolution Hi-C and Micro-C data, MO_SCPLOWUSTACHEC_SCPLOW detects loops at a wide range of genomic distances, identifying potential structural and regulatory interactions that are supported by independent conformation capture experiments as well as by known correlates of loop formation such as CTCF binding, enhancers and promoters. Unlike the commonly used HiCCUPS tool, MO_SCPLOWUSTACHEC_SCPLOW runs on general-purpose CPUs and it is very time efficient with a runtime of only a few minutes per chromosome for 5kb-resolution human genome contact maps. Extensive experimental results show that MO_SCPLOWUSTACHEC_SCPLOW reports two to three times the number of HiCCUPS loops, which are reproducible across replicates. It also recovers a larger proportion of published ChIA-PET and HiChIP loops than HiCCUPS. A comparative analysis of MO_SCPLOWUSTACHEC_SCPLOWs experimental results on Hi-C and Micro-C data confirms strong agreement between the two datasets with Micro-C providing better power for loop detection. Overall, our experimental results show that MO_SCPLOWUSTACHEC_SCPLOW enables a more efficient and comprehensive analysis of the chromatin looping from high-resolution Hi-C and Micro-C datasets. MO_SCPLOWUSTACHEC_SCPLOW is freely available at https://github.com/ay-lab/mustache.