Data from: Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies

Current Hi-C analysis approaches are unable to account for reads that
align to multiple locations, and hence underestimate biological signal
from repetitive regions of genomes. We developed and validated mHi-C, a
multi-read mapping strategy to probabilistically allocate Hi-C
multi-reads. mHi-C exhibited superior performance over utilizing only
uni-reads and heuristic approaches aimed at rescuing multi-reads on
benchmarks. Specifically, mHi-C increased the sequencing depth by an
average of 20% resulting in higher reproducibility of contact matrices and
detected interactions across biological replicates. The impact of the
multi-reads on the detection of significant interactions is influenced
marginally by the relative contribution of multi-reads to the sequencing
depth compared to uni-reads, cis-to-trans ratio of contacts, and the broad
data quality as reflected by the proportion of mappable reads of datasets.
Computational experiments highlighted that in Hi-C studies with short read
lengths, mHi-C rescued multi-reads can emulate the effect of longer reads.
mHi-C also revealed biologically supported bona fide promoter-enhancer
interactions and topologically associating domains involving repetitive
genomic regions, thereby unlocking a previously masked portion of the
genome for conformation capture studies.