DOI: 10.5061/dryad.v7k3140
title: Data from: Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies

description: 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.
description: p300 ChIP-seq peaks
 using uni- and multi-readsChIP-seq peaks detected
 following the standard ChIP-seq data processing pipeline of ENCODE (The
 ENCODE Project Consortium, 2012) using both uni-reads and multi-reads
 aligned by Permseq (Zeng et al., 2015) for p300.p300.zipp65 ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for P65.p65.zipPolII ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for PolII.PolII.zipH3K4me1 ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for H3K4me1.H3K4me1.zipCTCF ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for CTCF.CTCF.zipH3K4me3 ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for H3K4me3.H3K4me3.zipH3K27ac ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for H3K27ac.H3K27ac.zipH3K27me3 ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for H3K27me3.H3K27me3.zipH3K36me3 ChIP-seq peaks using uni- and multi-readsChIP-seq peaks detected following the standard ChIP-seq data processing pipeline of ENCODE (The ENCODE Project Consortium, 2012) using both uni-reads and multi-reads aligned by Permseq (Zeng et al., 2015) for H3K36me3.H3K36me3.zip