Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies
- University of Wisconsin-Madison, United States
- La Jolla Institute for Allergy and Immunology, United States
Abstract
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.
Data availability
GEO and ENCODE accession codes for all the data analyzed in this manuscript are provided in the manuscript.Source data files have been provided for Figures 1, 3, 4, and 5 (some via Dryad http://dx.doi.org/10.5061/dryad.v7k3140).The mHiC software is made available on github https://github.com/keleslab/mHiC with proper documentation.
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Data from: Generative Modeling of Multi-mapping Reads with mHi-C Advances Analysis of High Throughput Genome-wide Conformation Capture StudiesDryad Digital Repository, doi:10.5061/dryad.v7k3140.
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IMR90 Hi-C DatasetNCBI Gene Expression Omnibus, GSE43070.
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Plasmodium Hi-C DatasetNCBI Gene Expression Omnibus, GSE50199.
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GM12878 Hi-C DatasetNCBI Gene Expression Omnibus, GSE63525.
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ESC(2012) Hi-C DatasetNCBI Gene Expression Omnibus, GSE35156.
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A549 Hi-C DatasetNCBI Gene Expression Omnibus, GSE92819.
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ESC(2017) & Cortex Hi-C DatasetsNCBI Gene Expression Omnibus, GSE96107.
Article and author information
Author details
Funding
National Human Genome Research Institute (HG009744)
- Sunduz Keles
La Jolla Institute for Allergy and Immunology (Institute Leadership Funds)
- Ferhat Ay
National Human Genome Research Institute (HG007019)
- Sunduz Keles
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2019, Zheng et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies
eLife 8:e38070.
https://doi.org/10.7554/eLife.38070
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