Motivation: Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) is an established method for detecting genome-wide looping interactions at high resolution. Current ChIA-PET analysis software packages either fail to correct for non-specific interactions due to genomic proximity or only address a fraction of the steps required for data processing. We present Mango, a complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity.

Results: Comparison to the existing software packages, ChIA-PET Tool and ChiaSig, revealed that Mango interactions exhibit much better agreement with high-resolution Hi-C data. Importantly Mango executes all steps required for processing ChIA-PET data sets whereas ChiaSig only completes 20% of the required steps. Application of Mango to multiple available ChIA-PET data sets permitted the independent rediscovery of known trends in chromatin loops including enrichment of CTCF, RAD21, SMC3, and ZNF143 at the anchor regions of interactions as well as strong bias for convergent CTCF motifs.

Availability: Mango is open source and distributed through github at https://github.com/dphansti/mango.

Contact: *mpsnyder@standford.edu, Dept. of Genetics, MC: 5120, 300 Pasteur Dr., M-344, Stanford, CA 94305-5120