Generative modeling of multi-mapping reads with mHi-C advances analysis of Hi-C studies
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.
-
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.
-
IMR90 Hi-C DatasetNCBI Gene Expression Omnibus, GSE43070.
-
Plasmodium Hi-C DatasetNCBI Gene Expression Omnibus, GSE50199.
-
GM12878 Hi-C DatasetNCBI Gene Expression Omnibus, GSE63525.
-
ESC(2012) Hi-C DatasetNCBI Gene Expression Omnibus, GSE35156.
-
A549 Hi-C DatasetNCBI Gene Expression Omnibus, GSE92819.
-
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.
Reviewing Editor
- Bing Ren, University of California, San Diego School of Medicine, United States
Version history
- Received: May 3, 2018
- Accepted: January 30, 2019
- Accepted Manuscript published: January 31, 2019 (version 1)
- Version of Record published: April 5, 2019 (version 2)
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.
Metrics
-
- 3,693
- views
-
- 492
- downloads
-
- 18
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cell Biology
- Computational and Systems Biology
Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.
-
- Computational and Systems Biology
- Neuroscience
Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.