1. Computational and Systems Biology
Download icon

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

  1. Ye Zheng
  2. Ferhat Ay
  3. Sunduz Keles  Is a corresponding author
  1. University of Wisconsin-Madison, United States
  2. La Jolla Institute for Allergy and Immunology, United States
Tools and Resources
  • Cited 10
  • Views 2,695
  • Annotations
Cite this article as: eLife 2019;8:e38070 doi: 10.7554/eLife.38070

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.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Ye Zheng

    Department of Statistics, University of Wisconsin-Madison, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8806-2761
  2. Ferhat Ay

    La Jolla Institute for Allergy and Immunology, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sunduz Keles

    Department of Statistics, University of Wisconsin-Madison, Madison, United States
    For correspondence
    keles@stat.wisc.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9048-0922

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

  1. Bing Ren, University of California, San Diego School of Medicine, United States

Publication history

  1. Received: May 3, 2018
  2. Accepted: January 30, 2019
  3. Accepted Manuscript published: January 31, 2019 (version 1)
  4. 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

  • 2,695
    Page views
  • 413
    Downloads
  • 10
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Hannah R Meredith et al.
    Research Article

    Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

    1. Computational and Systems Biology
    Daniel Griffith, Alex S Holehouse
    Tools and Resources

    The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.